Evaluation of different biological data and computational classification methods for use in protein interaction prediction.
|
|
- Mark Page
- 5 years ago
- Views:
Transcription
1 Evaluation of different biological data and computational classification methods for use in protein interaction prediction. Yanjun Qi, Ziv Bar-Joseph, Judith Klein-Seetharaman Protein 2006
2 Motivation Correctly identifying the set of interacting proteins in an organism is useful for deciphering the molecular mechanisms underlying given biological functions and for assigning functions to unknown proteins based on their interacting partners.
3 Introduction Physical interaction
4 Introduction Co-complex relationship
5 Introduction Pathway co-membership
6 Introduction Lean mass protein complex
7 Introduction Lean mass protein complex NOT INCLUDED IN STUDY!
8 Introduction Yeast proteinprotein inteactions (Jeong et al. 2001)
9 Direct Methods of PPI Prediction Current high-throughput experimental approaches have been applied to determine the set of interacting proteins Two-hybird (Y2H) Mass Spectrometry
10 Direct Methods of PPI Prediction Current high-throughput experimental approaches have been applied to determine the set of interacting proteins Two-hybird (Y2H) Mass Spectrometry These methods have high rate of false-positves and false-negatives.
11 Direct Methods of PPI Prediction Two-hybird (Y2H)
12 Direct Methods of PPI Prediction Tandem Affinity Purification Mass Spectrometry
13 Indirect Methods of PPI Prediction Gene expression data
14 Indirect Methods of PPI Prediction Gene expression data Biological function (GO)
15 Indirect Methods of PPI Prediction Gene expression data Biological function (GO) Biological process (GO)
16 Indirect Methods of PPI Prediction Gene expression data Biological function (GO) Biological process (GO) Sequence similarity
17 Key Words PPI - protein protein interaction
18 Key Words PPI - protein protein interaction Gold Standard Dataset - data used to train and test an algorithm
19 Key Words PPI - protein protein interaction Gold Standard Dataset - data used to train and test an algorithm Positive Examples - a set of known interacting protein pairs
20 Key Words PPI - protein protein interaction Gold Standard Dataset - data used to train and test an algorithm Positive Examples - a set of known interacting protein pairs Negative Examples - a set of randomly paired proteins believed not to interact with each other
21 Key Words Feature Encoding - how do we use the data we have?
22 Key Words Feature Encoding - how do we use the data we have? Detailed - each source is handled separately
23 Key Words Feature Encoding - how do we use the data we have? Detailed - each source is handled separately Summary - combine similar sources
24 Goal Combine information from a variety of direct/indirect methods and apply them to a supervised learning framework and predict protein-protein interactions
25 Goal Combine information from a variety of direct/indirect methods and apply them to a supervised learning framework and predict protein-protein interactions Which one is the best?
26 Past Studies Varying datasets, encoding styles and classifiers Jansen et al. - Naïve Bayes, Co-complex, Summary Lin et al. - Random Forest, Logistic Regression, Co-complex, Summary Zhang et al. Decision Tree, Co-complex, Detailed etc
27 Past Studies Varying datasets, encoding styles and classifiers Jansen et al. - Naïve Bayes, Co-complex, Summary Lin et al. - Random Forest, Logistic Regression, Co-complex, Summary Zhang et al. Decision Tree, Co-complex, Detailed etc
28 Past Studies Varying datasets, encoding styles and classifiers Jansen et al. - Naïve Bayes, Co-complex, Summary Lin et al. - Random Forest, Logistic Regression, Co-complex, Summary Zhang et al. Decision Tree, Co-complex, Detailed etc
29 Past Studies Varying datasets, encoding styles and classifiers Jansen et al. - Naïve Bayes, Co-complex, Summary Lin et al. - Random Forest, Logistic Regression, Co-complex, Summary Zhang et al. Decision Tree, Co-complex, Detailed etc
30 Past Studies Varying datasets, encoding styles and classifiers Jansen et al. - Naïve Bayes, Co-complex, Summary Lin et al. - Random Forest, Logistic Regression, Co-complex, Summary Zhang et al. Decision Tree, Co-complex, Detailed etc
31 Systematic Comparison Reference Datasets = {physical, cocomplex, co-pathway} Encoding Styles = {summary, detailed} Classifiers = {DT, LR, NB, SVM, RF, krf}
32 Systematic Comparison Reference Datasets = {physical, cocomplex, co-pathway} Encoding Styles = {summary, detailed} Classifiers = {DT, LR, NB, SVM, RF, krf}
33 Positive Examples Physical Interactions - DIP (Database of Interacting Proteins)
34 Positive Examples Physical Interactions - DIP (Database of Interacting Proteins) Co-complex Interactions - MIPS (Munich Information Center for Protein Sequences)
35 Positive Examples Physical Interactions - DIP (Database of Interacting Proteins) Co-complex Interactions - MIPS (Munich Information Center for Protein Sequences) Co-pathway - KEGG (Kyoto Encyclopedia of Genes and Genomes)
36 Positive Examples
37 Negative Examples Post-filtering randomized protein pairing (Zhang et al. 2004) Only a fraction of of total pairs within the datasets are interacting, ~99% of randomized data is non-interacting
38 Negative Examples Post-filtering randomized protein pairing (Zhang et al. 2004) Only a fraction of of total pairs within the datasets are interacting, ~99% of randomized data is non-interacting Final training sets contained one positive example for every 600 negative interaction pairs
39 Features Used
40 Classification Algorithms SVM - Support Vector Machine NB - Naïve Bayes LR - Logistic Regression DT - Decision Tree RF - Random Forest krf - Random Forest-based k-nearest Neighbor
41 Support Vector Machine Basic idea of support vector machines Find optimal hyperplane for linearly separating patterns
42 Support Vector Machine Basic idea of support vector machines Find optimal hyperplane for linearly separating patterns Extend to patterns that are not linearly separable by transforming data into new space
43 Support Vector Support vectors are the data points that lie closest to the decision surface
44 Support Vector Support vectors are the data points that lie closest to the decision surface They have a direct bearing on the optimum location of the decision surface
45 Support Vector Machine
46 Support Vector Machine Y = mx + b
47 Support Vector Machine Y = mx + b
48 Support Vector Machine Y = mx + b
49 Support Vector Machine
50 Support Vector Machine r 2 = X 2 + Y 2
51 Support Vector Machine
52 Support Vector Machine As we move to higher dimensions the problem becomes much more complex
53 Naïve Bayes Basic idea of Naïve Bayes Calculate probability of a desired outcome based on a set of characteristics assuming a desired outcome
54 Naïve Bayes Basic idea of Naïve Bayes Calculate probability of a desired outcome based on a set of characteristics assuming a desired outcome Bayes rule
55 Bayes Rule B a Char 1 Char 2 Char 3 Char 4 Char 5 Interaction Y Y N N N Y Y N N Y Y N N N N Y Y N Y Y Y Y N Y N N Y Y N N
56 Naïve Bayes Take the product across all characteristics (X i ) with the assumption that each event is independent and that there is an interaction (Y = 1)
57 Logistic Regression Basic idea of Naïve Bayes Statistical regression model for binary dependant variables
58 Decision Tree Basic idea of tree based methods Construct a binary tree where each node represents a filter for a given characteristic and each leaf contains the decision Root contains all protein pairs and at each node pairs are separated into two categories, representing presence or absence of a characteristic
59 Decision Tree How do we decide which characteristic to use when separating data?
60 Decision Tree How do we decide which characteristic to use when separating data? Gini Index Looks at the largest class in the target, and tries to find a split, using a feature, to isolate it from the other classes
61 Decision Tree How do we decide which characteristic to use when separating data? Gini Index Looks at the largest class in the target, and tries to find a split, using a feature, to isolate it from the other classes A perfect series of splits would end up with k pure child nodes
62 Decision Tree How do we decide which characteristic to use when separating data? Gini Index Looks at the largest class in the target, and tries to find a split, using a feature, to isolate it from the other classes A perfect series of splits would end up with k pure child nodes If costs are assigned, we could isolate the most costly feature (most important), the one which tends to drive the cases into a single class
63 Decision Tree How do we decide which characteristic to use when separating data? Gini Index Looks at the largest class in the target, and tries to find a split, using a feature, to isolate it from the other classes A perfect series of splits would end up with k pure child nodes If costs are assigned, we could isolate the most costly feature (most important), the one which tends to drive the cases into a single class
64 Decision Tree Interaction?
65 Decision Tree Interaction? Similar Gene Expresssion
66 Decision Tree Interaction? Similar Gene Expression Sequence Similarity (95%) Go Annotation (Level 3) Y
67 Decision Tree Interaction? Similar Gene Expression Sequence Similarity (95%) Go Annotation (Level 3) Characteristic 1 Characteristic 2 Characteristic 3 N Y Y N Y N Y
68 Pruning After splitting stops the next step is prune the tree Cut off branches that provide the least additional predictive power Cut off weak branches with high misclassification rates
69 Pruning After splitting stops the next step is prune the tree Cut off branches that provide the least additional predictive power Cut off weak branches with high misclassification rates Improve accuracy
70 Decision Tree Interaction? Gene Regulation (2-fold) Sequence Similarity (95%) Go Annotation (Level 3) Characteristic 3 N Y N N Y
71 Random Forest Based on same idea as Decision Tree only we take random subsets of features and construct multiple trees simultaneously
72 Random Forest Based on same idea as Decision Tree only we take random subsets of features and construct multiple trees simultaneously Classification is chosen based on majority support 200 trees for each run
73 Random Forest
74 Random Forest
75 Random Forest Based on majority rule we would consider the pair tested as an interacting pair
76 k-nearest Neighbor Based on same idea as Random Forest only we calculate a similarity matrix based on the tree comparison values
77 k-nearest Neighbor Based on same idea as Random Forest only we calculate a similarity matrix based on the tree comparison values Classification is chosen based on k- nearest neighbors Do not specify the value of k used
78 k-nearest Neighbor <1,1,0,1,1, 0,1> Vector is used to plot data in n- dimensional space (n = 200)
79 k-nearest Neighbor
80 k-nearest Neighbor
81 k-nearest Neighbor
82 Performance Evaluation Decision model was trained with 30,000 protein pairs and then tested with a different 30,000
83 Performance Evaluation Decision model was trained with 30,000 protein pairs and then tested with a different 30,000 Plot precision vs recall Receiver operator characteristic curves (ROC)
84 Precision vs. Recall T Reality F Prediction T F True Positive (TP) False Negative Type II Error (FN) False Positive Type I Error (FP) True Negative (TN)
85 ROC Curves Plot of true-positives vs false positives
86 ROC Curves Plot of true-positives vs false positives Area under the curve is used as a measure of diagnostic accuracy Area measured until 50 false positives are found
87 Performance Comparison
88 Feature Importance Gene expression data is the most important in recovering all types of interactions
89 Feature Composition
90 Conclusions Co-complex relationships are the easiest to predict
91 Conclusions Co-complex relationships are the easiest to predict Detailed encoding style is preferred
92 Conclusions Co-complex relationships are the easiest to predict Detailed encoding style is preferred Random Forest classifier performs the best
93 Conclusions Co-complex relationships are the easiest to predict Detailed encoding style is preferred Random Forest classifier performs the best Different features have different importance in predicting protein interactions
94 Questions? Conclusions
Random Forest Similarity for Protein-Protein Interaction Prediction from Multiple Sources. Y. Qi, J. Klein-Seetharaman, and Z.
Random Forest Similarity for Protein-Protein Interaction Prediction from Multiple Sources Y. Qi, J. Klein-Seetharaman, and Z. Bar-Joseph Pacific Symposium on Biocomputing 10:531-542(2005) RANDOM FOREST
More informationNetwork Traffic Measurements and Analysis
DEIB - Politecnico di Milano Fall, 2017 Sources Hastie, Tibshirani, Friedman: The Elements of Statistical Learning James, Witten, Hastie, Tibshirani: An Introduction to Statistical Learning Andrew Ng:
More informationEvaluating Classifiers
Evaluating Classifiers Reading for this topic: T. Fawcett, An introduction to ROC analysis, Sections 1-4, 7 (linked from class website) Evaluating Classifiers What we want: Classifier that best predicts
More informationClassification Algorithms in Data Mining
August 9th, 2016 Suhas Mallesh Yash Thakkar Ashok Choudhary CIS660 Data Mining and Big Data Processing -Dr. Sunnie S. Chung Classification Algorithms in Data Mining Deciding on the classification algorithms
More informationEvaluating Classifiers
Evaluating Classifiers Reading for this topic: T. Fawcett, An introduction to ROC analysis, Sections 1-4, 7 (linked from class website) Evaluating Classifiers What we want: Classifier that best predicts
More informationSupervised vs unsupervised clustering
Classification Supervised vs unsupervised clustering Cluster analysis: Classes are not known a- priori. Classification: Classes are defined a-priori Sometimes called supervised clustering Extract useful
More informationPerformance Evaluation of Various Classification Algorithms
Performance Evaluation of Various Classification Algorithms Shafali Deora Amritsar College of Engineering & Technology, Punjab Technical University -----------------------------------------------------------***----------------------------------------------------------
More informationCS145: INTRODUCTION TO DATA MINING
CS145: INTRODUCTION TO DATA MINING 08: Classification Evaluation and Practical Issues Instructor: Yizhou Sun yzsun@cs.ucla.edu October 24, 2017 Learnt Prediction and Classification Methods Vector Data
More informationSupervised Learning Classification Algorithms Comparison
Supervised Learning Classification Algorithms Comparison Aditya Singh Rathore B.Tech, J.K. Lakshmipat University -------------------------------------------------------------***---------------------------------------------------------
More informationCS6375: Machine Learning Gautam Kunapuli. Mid-Term Review
Gautam Kunapuli Machine Learning Data is identically and independently distributed Goal is to learn a function that maps to Data is generated using an unknown function Learn a hypothesis that minimizes
More informationEvaluating Classifiers
Evaluating Classifiers Charles Elkan elkan@cs.ucsd.edu January 18, 2011 In a real-world application of supervised learning, we have a training set of examples with labels, and a test set of examples with
More informationIEE 520 Data Mining. Project Report. Shilpa Madhavan Shinde
IEE 520 Data Mining Project Report Shilpa Madhavan Shinde Contents I. Dataset Description... 3 II. Data Classification... 3 III. Class Imbalance... 5 IV. Classification after Sampling... 5 V. Final Model...
More informationCSE 158. Web Mining and Recommender Systems. Midterm recap
CSE 158 Web Mining and Recommender Systems Midterm recap Midterm on Wednesday! 5:10 pm 6:10 pm Closed book but I ll provide a similar level of basic info as in the last page of previous midterms CSE 158
More informationStatistics 202: Statistical Aspects of Data Mining
Statistics 202: Statistical Aspects of Data Mining Professor Rajan Patel Lecture 9 = More of Chapter 5 Agenda: 1) Lecture over more of Chapter 5 1 Introduction to Data Mining by Tan, Steinbach, Kumar Chapter
More informationWeka ( )
Weka ( http://www.cs.waikato.ac.nz/ml/weka/ ) The phases in which classifier s design can be divided are reflected in WEKA s Explorer structure: Data pre-processing (filtering) and representation Supervised
More informationECLT 5810 Evaluation of Classification Quality
ECLT 5810 Evaluation of Classification Quality Reference: Data Mining Practical Machine Learning Tools and Techniques, by I. Witten, E. Frank, and M. Hall, Morgan Kaufmann Testing and Error Error rate:
More informationEvaluation Measures. Sebastian Pölsterl. April 28, Computer Aided Medical Procedures Technische Universität München
Evaluation Measures Sebastian Pölsterl Computer Aided Medical Procedures Technische Universität München April 28, 2015 Outline 1 Classification 1. Confusion Matrix 2. Receiver operating characteristics
More informationData Mining and Knowledge Discovery Practice notes 2
Keywords Data Mining and Knowledge Discovery: Practice Notes Petra Kralj Novak Petra.Kralj.Novak@ijs.si Data Attribute, example, attribute-value data, target variable, class, discretization Algorithms
More informationList of Exercises: Data Mining 1 December 12th, 2015
List of Exercises: Data Mining 1 December 12th, 2015 1. We trained a model on a two-class balanced dataset using five-fold cross validation. One person calculated the performance of the classifier by measuring
More informationClassification and Regression
Classification and Regression Announcements Study guide for exam is on the LMS Sample exam will be posted by Monday Reminder that phase 3 oral presentations are being held next week during workshops Plan
More informationData Mining Concepts & Techniques
Data Mining Concepts & Techniques Lecture No. 03 Data Processing, Data Mining Naeem Ahmed Email: naeemmahoto@gmail.com Department of Software Engineering Mehran Univeristy of Engineering and Technology
More informationData Mining and Knowledge Discovery: Practice Notes
Data Mining and Knowledge Discovery: Practice Notes Petra Kralj Novak Petra.Kralj.Novak@ijs.si 8.11.2017 1 Keywords Data Attribute, example, attribute-value data, target variable, class, discretization
More informationDATA MINING INTRODUCTION TO CLASSIFICATION USING LINEAR CLASSIFIERS
DATA MINING INTRODUCTION TO CLASSIFICATION USING LINEAR CLASSIFIERS 1 Classification: Definition Given a collection of records (training set ) Each record contains a set of attributes and a class attribute
More informationIdentifying and Understanding Differential Transcriptor Binding
Identifying and Understanding Differential Transcriptor Binding 15-899: Computational Genomics David Koes Yong Lu Motivation Under different conditions, a transcription factor binds to different genes
More informationApplying Supervised Learning
Applying Supervised Learning When to Consider Supervised Learning A supervised learning algorithm takes a known set of input data (the training set) and known responses to the data (output), and trains
More informationProbabilistic Classifiers DWML, /27
Probabilistic Classifiers DWML, 2007 1/27 Probabilistic Classifiers Conditional class probabilities Id. Savings Assets Income Credit risk 1 Medium High 75 Good 2 Low Low 50 Bad 3 High Medium 25 Bad 4 Medium
More informationClassifiers and Detection. D.A. Forsyth
Classifiers and Detection D.A. Forsyth Classifiers Take a measurement x, predict a bit (yes/no; 1/-1; 1/0; etc) Detection with a classifier Search all windows at relevant scales Prepare features Classify
More informationA Comparative Study of Locality Preserving Projection and Principle Component Analysis on Classification Performance Using Logistic Regression
Journal of Data Analysis and Information Processing, 2016, 4, 55-63 Published Online May 2016 in SciRes. http://www.scirp.org/journal/jdaip http://dx.doi.org/10.4236/jdaip.2016.42005 A Comparative Study
More informationCS4491/CS 7265 BIG DATA ANALYTICS
CS4491/CS 7265 BIG DATA ANALYTICS EVALUATION * Some contents are adapted from Dr. Hung Huang and Dr. Chengkai Li at UT Arlington Dr. Mingon Kang Computer Science, Kennesaw State University Evaluation for
More informationContents Machine Learning concepts 4 Learning Algorithm 4 Predictive Model (Model) 4 Model, Classification 4 Model, Regression 4 Representation
Contents Machine Learning concepts 4 Learning Algorithm 4 Predictive Model (Model) 4 Model, Classification 4 Model, Regression 4 Representation Learning 4 Supervised Learning 4 Unsupervised Learning 4
More informationClassification Part 4
Classification Part 4 Dr. Sanjay Ranka Professor Computer and Information Science and Engineering University of Florida, Gainesville Model Evaluation Metrics for Performance Evaluation How to evaluate
More informationArtificial Intelligence. Programming Styles
Artificial Intelligence Intro to Machine Learning Programming Styles Standard CS: Explicitly program computer to do something Early AI: Derive a problem description (state) and use general algorithms to
More informationDATA MINING LECTURE 11. Classification Basic Concepts Decision Trees Evaluation Nearest-Neighbor Classifier
DATA MINING LECTURE 11 Classification Basic Concepts Decision Trees Evaluation Nearest-Neighbor Classifier What is a hipster? Examples of hipster look A hipster is defined by facial hair Hipster or Hippie?
More informationMachine Learning for. Artem Lind & Aleskandr Tkachenko
Machine Learning for Object Recognition Artem Lind & Aleskandr Tkachenko Outline Problem overview Classification demo Examples of learning algorithms Probabilistic modeling Bayes classifier Maximum margin
More informationCOSC160: Detection and Classification. Jeremy Bolton, PhD Assistant Teaching Professor
COSC160: Detection and Classification Jeremy Bolton, PhD Assistant Teaching Professor Outline I. Problem I. Strategies II. Features for training III. Using spatial information? IV. Reducing dimensionality
More informationAdvanced Video Content Analysis and Video Compression (5LSH0), Module 8B
Advanced Video Content Analysis and Video Compression (5LSH0), Module 8B 1 Supervised learning Catogarized / labeled data Objects in a picture: chair, desk, person, 2 Classification Fons van der Sommen
More informationCS249: ADVANCED DATA MINING
CS249: ADVANCED DATA MINING Classification Evaluation and Practical Issues Instructor: Yizhou Sun yzsun@cs.ucla.edu April 24, 2017 Homework 2 out Announcements Due May 3 rd (11:59pm) Course project proposal
More informationClustering will not be satisfactory if:
Clustering will not be satisfactory if: -- in the input space the clusters are not linearly separable; -- the distance measure is not adequate; -- the assumptions limit the shape or the number of the clusters.
More informationBusiness Club. Decision Trees
Business Club Decision Trees Business Club Analytics Team December 2017 Index 1. Motivation- A Case Study 2. The Trees a. What is a decision tree b. Representation 3. Regression v/s Classification 4. Building
More informationCSE4334/5334 DATA MINING
CSE4334/5334 DATA MINING Lecture 4: Classification (1) CSE4334/5334 Data Mining, Fall 2014 Department of Computer Science and Engineering, University of Texas at Arlington Chengkai Li (Slides courtesy
More informationInformation Management course
Università degli Studi di Milano Master Degree in Computer Science Information Management course Teacher: Alberto Ceselli Lecture 20: 10/12/2015 Data Mining: Concepts and Techniques (3 rd ed.) Chapter
More informationArtificial Neural Networks (Feedforward Nets)
Artificial Neural Networks (Feedforward Nets) y w 03-1 w 13 y 1 w 23 y 2 w 01 w 21 w 22 w 02-1 w 11 w 12-1 x 1 x 2 6.034 - Spring 1 Single Perceptron Unit y w 0 w 1 w n w 2 w 3 x 0 =1 x 1 x 2 x 3... x
More informationData Mining and Knowledge Discovery: Practice Notes
Data Mining and Knowledge Discovery: Practice Notes Petra Kralj Novak Petra.Kralj.Novak@ijs.si 2016/11/16 1 Keywords Data Attribute, example, attribute-value data, target variable, class, discretization
More informationNearest neighbor classification DSE 220
Nearest neighbor classification DSE 220 Decision Trees Target variable Label Dependent variable Output space Person ID Age Gender Income Balance Mortgag e payment 123213 32 F 25000 32000 Y 17824 49 M 12000-3000
More informationCS 229 Midterm Review
CS 229 Midterm Review Course Staff Fall 2018 11/2/2018 Outline Today: SVMs Kernels Tree Ensembles EM Algorithm / Mixture Models [ Focus on building intuition, less so on solving specific problems. Ask
More informationDATA MINING LECTURE 9. Classification Basic Concepts Decision Trees Evaluation
DATA MINING LECTURE 9 Classification Basic Concepts Decision Trees Evaluation What is a hipster? Examples of hipster look A hipster is defined by facial hair Hipster or Hippie? Facial hair alone is not
More informationLecture 25: Review I
Lecture 25: Review I Reading: Up to chapter 5 in ISLR. STATS 202: Data mining and analysis Jonathan Taylor 1 / 18 Unsupervised learning In unsupervised learning, all the variables are on equal standing,
More informationNaïve Bayes Classification. Material borrowed from Jonathan Huang and I. H. Witten s and E. Frank s Data Mining and Jeremy Wyatt and others
Naïve Bayes Classification Material borrowed from Jonathan Huang and I. H. Witten s and E. Frank s Data Mining and Jeremy Wyatt and others Things We d Like to Do Spam Classification Given an email, predict
More informationSupplementary Material
Supplementary Material Figure 1S: Scree plot of the 400 dimensional data. The Figure shows the 20 largest eigenvalues of the (normalized) correlation matrix sorted in decreasing order; the insert shows
More informationPartitioning Data. IRDS: Evaluation, Debugging, and Diagnostics. Cross-Validation. Cross-Validation for parameter tuning
Partitioning Data IRDS: Evaluation, Debugging, and Diagnostics Charles Sutton University of Edinburgh Training Validation Test Training : Running learning algorithms Validation : Tuning parameters of learning
More informationCLASSIFICATION JELENA JOVANOVIĆ. Web:
CLASSIFICATION JELENA JOVANOVIĆ Email: jeljov@gmail.com Web: http://jelenajovanovic.net OUTLINE What is classification? Binary and multiclass classification Classification algorithms Naïve Bayes (NB) algorithm
More informationTutorials Case studies
1. Subject Three curves for the evaluation of supervised learning methods. Evaluation of classifiers is an important step of the supervised learning process. We want to measure the performance of the classifier.
More information.. Cal Poly CSC 466: Knowledge Discovery from Data Alexander Dekhtyar.. for each element of the dataset we are given its class label.
.. Cal Poly CSC 466: Knowledge Discovery from Data Alexander Dekhtyar.. Data Mining: Classification/Supervised Learning Definitions Data. Consider a set A = {A 1,...,A n } of attributes, and an additional
More informationCLASSIFICATION WITH RADIAL BASIS AND PROBABILISTIC NEURAL NETWORKS
CLASSIFICATION WITH RADIAL BASIS AND PROBABILISTIC NEURAL NETWORKS CHAPTER 4 CLASSIFICATION WITH RADIAL BASIS AND PROBABILISTIC NEURAL NETWORKS 4.1 Introduction Optical character recognition is one of
More informationNaïve Bayes Classification. Material borrowed from Jonathan Huang and I. H. Witten s and E. Frank s Data Mining and Jeremy Wyatt and others
Naïve Bayes Classification Material borrowed from Jonathan Huang and I. H. Witten s and E. Frank s Data Mining and Jeremy Wyatt and others Things We d Like to Do Spam Classification Given an email, predict
More informationEvaluation Metrics. (Classifiers) CS229 Section Anand Avati
Evaluation Metrics (Classifiers) CS Section Anand Avati Topics Why? Binary classifiers Metrics Rank view Thresholding Confusion Matrix Point metrics: Accuracy, Precision, Recall / Sensitivity, Specificity,
More informationUsing Real-valued Meta Classifiers to Integrate and Contextualize Binding Site Predictions
Using Real-valued Meta Classifiers to Integrate and Contextualize Binding Site Predictions Offer Sharabi, Yi Sun, Mark Robinson, Rod Adams, Rene te Boekhorst, Alistair G. Rust, Neil Davey University of
More informationRandom Forest A. Fornaser
Random Forest A. Fornaser alberto.fornaser@unitn.it Sources Lecture 15: decision trees, information theory and random forests, Dr. Richard E. Turner Trees and Random Forests, Adele Cutler, Utah State University
More informationLarge Scale Data Analysis Using Deep Learning
Large Scale Data Analysis Using Deep Learning Machine Learning Basics - 1 U Kang Seoul National University U Kang 1 In This Lecture Overview of Machine Learning Capacity, overfitting, and underfitting
More informationIntroduction to Machine Learning CANB 7640
Introduction to Machine Learning CANB 7640 Aik Choon Tan, Ph.D. Associate Professor of Bioinformatics Division of Medical Oncology Department of Medicine aikchoon.tan@ucdenver.edu 9/5/2017 http://tanlab.ucdenver.edu/labhomepage/teaching/canb7640/
More informationContents. Preface to the Second Edition
Preface to the Second Edition v 1 Introduction 1 1.1 What Is Data Mining?....................... 4 1.2 Motivating Challenges....................... 5 1.3 The Origins of Data Mining....................
More informationFast or furious? - User analysis of SF Express Inc
CS 229 PROJECT, DEC. 2017 1 Fast or furious? - User analysis of SF Express Inc Gege Wen@gegewen, Yiyuan Zhang@yiyuan12, Kezhen Zhao@zkz I. MOTIVATION The motivation of this project is to predict the likelihood
More informationClassifying Imbalanced Data Sets Using. Similarity Based Hierarchical Decomposition
Classifying Imbalanced Data Sets Using Similarity Based Hierarchical Decomposition Cigdem BEYAN (Corresponding author), Robert FISHER School of Informatics, University of Edinburgh, G.12 Informatics Forum,
More informationData Mining in Bioinformatics Day 1: Classification
Data Mining in Bioinformatics Day 1: Classification Karsten Borgwardt February 18 to March 1, 2013 Machine Learning & Computational Biology Research Group Max Planck Institute Tübingen and Eberhard Karls
More informationLogistic Regression: Probabilistic Interpretation
Logistic Regression: Probabilistic Interpretation Approximate 0/1 Loss Logistic Regression Adaboost (z) SVM Solution: Approximate 0/1 loss with convex loss ( surrogate loss) 0-1 z = y w x SVM (hinge),
More informationMulti-label classification using rule-based classifier systems
Multi-label classification using rule-based classifier systems Shabnam Nazmi (PhD candidate) Department of electrical and computer engineering North Carolina A&T state university Advisor: Dr. A. Homaifar
More informationComputer Vision Group Prof. Daniel Cremers. 8. Boosting and Bagging
Prof. Daniel Cremers 8. Boosting and Bagging Repetition: Regression We start with a set of basis functions (x) =( 0 (x), 1(x),..., M 1(x)) x 2 í d The goal is to fit a model into the data y(x, w) =w T
More informationSubject. Dataset. Copy paste feature of the diagram. Importing the dataset. Copy paste feature into the diagram.
Subject Copy paste feature into the diagram. When we define the data analysis process into Tanagra, it is possible to copy components (or entire branches of components) towards another location into the
More informationSupplemental Material: Multi-Class Open Set Recognition Using Probability of Inclusion
Supplemental Material: Multi-Class Open Set Recognition Using Probability of Inclusion Lalit P. Jain, Walter J. Scheirer,2, and Terrance E. Boult,3 University of Colorado Colorado Springs 2 Harvard University
More informationUsing Machine Learning to Identify Security Issues in Open-Source Libraries. Asankhaya Sharma Yaqin Zhou SourceClear
Using Machine Learning to Identify Security Issues in Open-Source Libraries Asankhaya Sharma Yaqin Zhou SourceClear Outline - Overview of problem space Unidentified security issues How Machine Learning
More informationMIT Samberg Center Cambridge, MA, USA. May 30 th June 2 nd, by C. Rea, R.S. Granetz MIT Plasma Science and Fusion Center, Cambridge, MA, USA
Exploratory Machine Learning studies for disruption prediction on DIII-D by C. Rea, R.S. Granetz MIT Plasma Science and Fusion Center, Cambridge, MA, USA Presented at the 2 nd IAEA Technical Meeting on
More informationI211: Information infrastructure II
Data Mining: Classifier Evaluation I211: Information infrastructure II 3-nearest neighbor labeled data find class labels for the 4 data points 1 0 0 6 0 0 0 5 17 1.7 1 1 4 1 7.1 1 1 1 0.4 1 2 1 3.0 0 0.1
More informationCREDIT RISK MODELING IN R. Finding the right cut-off: the strategy curve
CREDIT RISK MODELING IN R Finding the right cut-off: the strategy curve Constructing a confusion matrix > predict(log_reg_model, newdata = test_set, type = "response") 1 2 3 4 5 0.08825517 0.3502768 0.28632298
More informationClassification. Slide sources:
Classification Slide sources: Gideon Dror, Academic College of TA Yaffo Nathan Ifill, Leicester MA4102 Data Mining and Neural Networks Andrew Moore, CMU : http://www.cs.cmu.edu/~awm/tutorials 1 Outline
More informationLouis Fourrier Fabien Gaie Thomas Rolf
CS 229 Stay Alert! The Ford Challenge Louis Fourrier Fabien Gaie Thomas Rolf Louis Fourrier Fabien Gaie Thomas Rolf 1. Problem description a. Goal Our final project is a recent Kaggle competition submitted
More informationCPSC 340: Machine Learning and Data Mining. More Linear Classifiers Fall 2017
CPSC 340: Machine Learning and Data Mining More Linear Classifiers Fall 2017 Admin Assignment 3: Due Friday of next week. Midterm: Can view your exam during instructor office hours next week, or after
More informationPart II: A broader view
Part II: A broader view Understanding ML metrics: isometrics, basic types of linear isometric plots linear metrics and equivalences between them skew-sensitivity non-linear metrics Model manipulation:
More informationADVANCED CLASSIFICATION TECHNIQUES
Admin ML lab next Monday Project proposals: Sunday at 11:59pm ADVANCED CLASSIFICATION TECHNIQUES David Kauchak CS 159 Fall 2014 Project proposal presentations Machine Learning: A Geometric View 1 Apples
More informationMachine Learning in Biology
Università degli studi di Padova Machine Learning in Biology Luca Silvestrin (Dottorando, XXIII ciclo) Supervised learning Contents Class-conditional probability density Linear and quadratic discriminant
More informationClassification and Regression Trees
Classification and Regression Trees Matthew S. Shotwell, Ph.D. Department of Biostatistics Vanderbilt University School of Medicine Nashville, TN, USA March 16, 2018 Introduction trees partition feature
More informationINTRODUCTION TO MACHINE LEARNING. Measuring model performance or error
INTRODUCTION TO MACHINE LEARNING Measuring model performance or error Is our model any good? Context of task Accuracy Computation time Interpretability 3 types of tasks Classification Regression Clustering
More informationEvaluating Machine-Learning Methods. Goals for the lecture
Evaluating Machine-Learning Methods Mark Craven and David Page Computer Sciences 760 Spring 2018 www.biostat.wisc.edu/~craven/cs760/ Some of the slides in these lectures have been adapted/borrowed from
More informationCS229 Lecture notes. Raphael John Lamarre Townshend
CS229 Lecture notes Raphael John Lamarre Townshend Decision Trees We now turn our attention to decision trees, a simple yet flexible class of algorithms. We will first consider the non-linear, region-based
More informationUser Guide Written By Yasser EL-Manzalawy
User Guide Written By Yasser EL-Manzalawy 1 Copyright Gennotate development team Introduction As large amounts of genome sequence data are becoming available nowadays, the development of reliable and efficient
More informationCSE 190, Spring 2015: Midterm
CSE 190, Spring 2015: Midterm Name: Student ID: Instructions Hand in your solution at or before 7:45pm. Answers should be written directly in the spaces provided. Do not open or start the test before instructed
More informationA novel supervised learning algorithm and its use for Spam Detection in Social Bookmarking Systems
A novel supervised learning algorithm and its use for Spam Detection in Social Bookmarking Systems Anestis Gkanogiannis and Theodore Kalamboukis Department of Informatics Athens University of Economics
More informationAn introduction to random forests
An introduction to random forests Eric Debreuve / Team Morpheme Institutions: University Nice Sophia Antipolis / CNRS / Inria Labs: I3S / Inria CRI SA-M / ibv Outline Machine learning Decision tree Random
More informationSUPERVISED LEARNING METHODS. Stanley Liang, PhD Candidate, Lassonde School of Engineering, York University Helix Science Engagement Programs 2018
SUPERVISED LEARNING METHODS Stanley Liang, PhD Candidate, Lassonde School of Engineering, York University Helix Science Engagement Programs 2018 2 CHOICE OF ML You cannot know which algorithm will work
More informationClassification. Instructor: Wei Ding
Classification Part II Instructor: Wei Ding Tan,Steinbach, Kumar Introduction to Data Mining 4/18/004 1 Practical Issues of Classification Underfitting and Overfitting Missing Values Costs of Classification
More informationDecision Trees Dr. G. Bharadwaja Kumar VIT Chennai
Decision Trees Decision Tree Decision Trees (DTs) are a nonparametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target
More informationApplication of Support Vector Machine In Bioinformatics
Application of Support Vector Machine In Bioinformatics V. K. Jayaraman Scientific and Engineering Computing Group CDAC, Pune jayaramanv@cdac.in Arun Gupta Computational Biology Group AbhyudayaTech, Indore
More informationIntroduction to Automated Text Analysis. bit.ly/poir599
Introduction to Automated Text Analysis Pablo Barberá School of International Relations University of Southern California pablobarbera.com Lecture materials: bit.ly/poir599 Today 1. Solutions for last
More informationML4Bio Lecture #1: Introduc3on. February 24 th, 2016 Quaid Morris
ML4Bio Lecture #1: Introduc3on February 24 th, 216 Quaid Morris Course goals Prac3cal introduc3on to ML Having a basic grounding in the terminology and important concepts in ML; to permit self- study,
More informationR (2) Data analysis case study using R for readily available data set using any one machine learning algorithm.
Assignment No. 4 Title: SD Module- Data Science with R Program R (2) C (4) V (2) T (2) Total (10) Dated Sign Data analysis case study using R for readily available data set using any one machine learning
More informationSupervised Learning. Decision trees Artificial neural nets K-nearest neighbor Support vectors Linear regression Logistic regression...
Supervised Learning Decision trees Artificial neural nets K-nearest neighbor Support vectors Linear regression Logistic regression... Supervised Learning y=f(x): true function (usually not known) D: training
More informationDecision trees. Decision trees are useful to a large degree because of their simplicity and interpretability
Decision trees A decision tree is a method for classification/regression that aims to ask a few relatively simple questions about an input and then predicts the associated output Decision trees are useful
More informationPredictive modelling / Machine Learning Course on Big Data Analytics
Predictive modelling / Machine Learning Course on Big Data Analytics Roberta Turra, Cineca 19 September 2016 Going back to the definition of data analytics process of extracting valuable information from
More informationMetrics for Performance Evaluation How to evaluate the performance of a model? Methods for Performance Evaluation How to obtain reliable estimates?
Model Evaluation Metrics for Performance Evaluation How to evaluate the performance of a model? Methods for Performance Evaluation How to obtain reliable estimates? Methods for Model Comparison How to
More informationEPL451: Data Mining on the Web Lab 5
EPL451: Data Mining on the Web Lab 5 Παύλος Αντωνίου Γραφείο: B109, ΘΕΕ01 University of Cyprus Department of Computer Science Predictive modeling techniques IBM reported in June 2012 that 90% of data available
More informationCSE Data Mining Concepts and Techniques STATISTICAL METHODS (REGRESSION) Professor- Anita Wasilewska. Team 13
CSE 634 - Data Mining Concepts and Techniques STATISTICAL METHODS Professor- Anita Wasilewska (REGRESSION) Team 13 Contents Linear Regression Logistic Regression Bias and Variance in Regression Model Fit
More information