Scheme:weka.classifiers.functions.MultilayerPerceptron -L 0.3 -M 0.2 -N 500 -V 0 -S E 20 -H a

Size: px
Start display at page:

Download "Scheme:weka.classifiers.functions.MultilayerPerceptron -L 0.3 -M 0.2 -N 500 -V 0 -S E 20 -H a"

Transcription

1 - Rede Neural 1 === Run information === Scheme:weka.classifiers.functions.MultilayerPerceptron -L 0.3 -M 0.2 -N 500 -V 0 -S E 20 -H a Relation: Aula9_RedesNeurais-2 Instances: 91 Attributes: 7 CLASS_Y LS GA Rep_EST Rep_PC EST_CUST FORN_VEN Test mode:10-fold cross-validation === Classifier model (full training set) === Sigmoid Node 0 Threshold Node Node Node Node Sigmoid Node 1 Threshold Node

2 Node Node Node Sigmoid Node 2 Threshold Attrib LS Attrib GA Attrib Rep_EST Attrib Rep_PC Attrib EST_CUST Attrib FORN_VEN Sigmoid Node 3 Threshold Attrib LS Attrib GA Attrib Rep_EST Attrib Rep_PC Attrib EST_CUST Attrib FORN_VEN Sigmoid Node 4 Threshold Attrib LS Attrib GA Attrib Rep_EST Attrib Rep_PC Attrib EST_CUST Attrib FORN_VEN Sigmoid Node 5

3 Threshold Attrib LS Attrib GA Attrib Rep_EST Attrib Rep_PC Attrib EST_CUST Attrib FORN_VEN Class I Node 0 Class A Node 1 Time taken to build model: 0.22 seconds === Stratified cross-validation === === Summary === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances 91 === Detailed Accuracy By Class ===

4 TP Rate FP Rate Precision Recall F-Measure ROC Area Class I A Weighted Avg === Confusion Matrix === a b <-- classified as 40 9 a = I 4 38 b = A - Rede Neural 2 === Run information === Scheme:weka.classifiers.functions.MultilayerPerceptron -L 0.9 -M 0.2 -N 500 -V 0 -S E 20 -H a Relation: Aula9_RedesNeurais-2 Instances: 91 Attributes: 7 CLASS_Y LS GA Rep_EST Rep_PC EST_CUST FORN_VEN Test mode:10-fold cross-validation

5 === Classifier model (full training set) === Sigmoid Node 0 Threshold Node Node Node Node Sigmoid Node 1 Threshold Node Node Node Node Sigmoid Node 2 Threshold Attrib LS Attrib GA Attrib Rep_EST Attrib Rep_PC Attrib EST_CUST Attrib FORN_VEN Sigmoid Node 3 Threshold Attrib LS Attrib GA

6 Attrib Rep_EST Attrib Rep_PC Attrib EST_CUST Attrib FORN_VEN Sigmoid Node 4 Threshold Attrib LS Attrib GA Attrib Rep_EST Attrib Rep_PC Attrib EST_CUST Attrib FORN_VEN Sigmoid Node 5 Threshold Attrib LS Attrib GA Attrib Rep_EST Attrib Rep_PC Attrib EST_CUST Attrib FORN_VEN Class I Node 0 Class A Node 1 Time taken to build model: 0.14 seconds

7 === Stratified cross-validation === === Summary === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances 91 === Detailed Accuracy By Class === TP Rate FP Rate Precision Recall F-Measure ROC Area Class I A Weighted Avg === Confusion Matrix === a b <-- classified as 40 9 a = I 2 40 b = A - Rede Neural 3 === Run information ===

8 Scheme:weka.classifiers.functions.MultilayerPerceptron -L 0.2 -M 0.2 -N V 0 -S E 20 -H a Relation: Aula9_RedesNeurais-2 Instances: 91 Attributes: 7 CLASS_Y LS GA Rep_EST Rep_PC EST_CUST FORN_VEN Test mode:10-fold cross-validation === Classifier model (full training set) === Sigmoid Node 0 Threshold Node Node Node Node Sigmoid Node 1 Threshold Node Node Node Node Sigmoid Node 2

9 Threshold Attrib LS Attrib GA Attrib Rep_EST Attrib Rep_PC Attrib EST_CUST Attrib FORN_VEN Sigmoid Node 3 Threshold Attrib LS Attrib GA Attrib Rep_EST Attrib Rep_PC Attrib EST_CUST Attrib FORN_VEN Sigmoid Node 4 Threshold Attrib LS Attrib GA Attrib Rep_EST Attrib Rep_PC Attrib EST_CUST Attrib FORN_VEN Sigmoid Node 5 Threshold Attrib LS Attrib GA

10 Attrib Rep_EST Attrib Rep_PC Attrib EST_CUST Attrib FORN_VEN Class I Node 0 Class A Node 1 Time taken to build model: 1.39 seconds === Stratified cross-validation === === Summary === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances 91 === Detailed Accuracy By Class === TP Rate FP Rate Precision Recall F-Measure ROC Area Class I A

11 Weighted Avg === Confusion Matrix === a b <-- classified as 42 7 a = I 3 39 b = A Análise: O modelo 3 apresenta melhor resultado, pois tem o maior acerto (89,01%) e o menor erro relativo médio (23,49%)

IEE 520 Data Mining. Project Report. Shilpa Madhavan Shinde

IEE 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 information

Why MultiLayer Perceptron/Neural Network? Objective: Attributes:

Why MultiLayer Perceptron/Neural Network? Objective: Attributes: Why MultiLayer Perceptron/Neural Network? Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are

More information

S2 Text. Instructions to replicate classification results.

S2 Text. Instructions to replicate classification results. S2 Text. Instructions to replicate classification results. Machine Learning (ML) Models were implemented using WEKA software Version 3.8. The software can be free downloaded at this link: http://www.cs.waikato.ac.nz/ml/weka/downloading.html.

More information

Classification Part 4

Classification 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 information

More on Learning. Neural Nets Support Vectors Machines Unsupervised Learning (Clustering) K-Means Expectation-Maximization

More on Learning. Neural Nets Support Vectors Machines Unsupervised Learning (Clustering) K-Means Expectation-Maximization More on Learning Neural Nets Support Vectors Machines Unsupervised Learning (Clustering) K-Means Expectation-Maximization Neural Net Learning Motivated by studies of the brain. A network of artificial

More information

Evaluation Measures. Sebastian Pölsterl. April 28, Computer Aided Medical Procedures Technische Universität München

Evaluation 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 information

DATA MINING LAB MANUAL

DATA MINING LAB MANUAL DATA MINING LAB MANUAL Subtasks : 1. List all the categorical (or nominal) attributes and the real-valued attributes seperately. Attributes:- 1. checking_status 2. duration 3. credit history 4. purpose

More information

Evaluating Classifiers

Evaluating 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 information

IMPLEMENTATION OF CLASSIFICATION ALGORITHMS USING WEKA NAÏVE BAYES CLASSIFIER

IMPLEMENTATION OF CLASSIFICATION ALGORITHMS USING WEKA NAÏVE BAYES CLASSIFIER IMPLEMENTATION OF CLASSIFICATION ALGORITHMS USING WEKA NAÏVE BAYES CLASSIFIER N. Suresh Kumar, Dr. M. Thangamani 1 Assistant Professor, Sri Ramakrishna Engineering College, Coimbatore, India 2 Assistant

More information

CS145: INTRODUCTION TO DATA MINING

CS145: 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 information

Evaluating Classifiers

Evaluating 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 information

The Explorer. chapter Getting started

The Explorer. chapter Getting started chapter 10 The Explorer Weka s main graphical user interface, the Explorer, gives access to all its facilities using menu selection and form filling. It is illustrated in Figure 10.1. There are six different

More information

CS4491/CS 7265 BIG DATA ANALYTICS

CS4491/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 information

Weka ( )

Weka (  ) 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 information

PROJECT 1 DATA ANALYSIS (KR-VS-KP)

PROJECT 1 DATA ANALYSIS (KR-VS-KP) PROJECT 1 DATA ANALYSIS (KR-VS-KP) Author: Tomáš Píhrt (xpiht00@vse.cz) Date: 12. 12. 2015 Contents 1 Introduction... 1 1.1 Data description... 1 1.2 Attributes... 2 1.3 Data pre-processing & preparation...

More information

A Study of Random Forest Algorithm with implemetation using Weka

A Study of Random Forest Algorithm with implemetation using Weka A Study of Random Forest Algorithm with implemetation using Weka 1 Dr. N.Venkatesan, Associate Professor, Dept. of IT., Bharathiyar College of Engg. & Technology, Karikal, India 2 Mrs. G.Priya, Assistant

More information

Evaluating Machine Learning Methods: Part 1

Evaluating Machine Learning Methods: Part 1 Evaluating Machine Learning Methods: Part 1 CS 760@UW-Madison Goals for the lecture you should understand the following concepts bias of an estimator learning curves stratified sampling cross validation

More information

Machine Learning and Bioinformatics 機器學習與生物資訊學

Machine Learning and Bioinformatics 機器學習與生物資訊學 Molecular Biomedical Informatics 分子生醫資訊實驗室 機器學習與生物資訊學 Machine Learning & Bioinformatics 1 Evaluation The key to success 2 Three datasets of which the answers must be known 3 Note on parameter tuning It

More information

List of Exercises: Data Mining 1 December 12th, 2015

List 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 information

Evaluating Machine-Learning Methods. Goals for the lecture

Evaluating 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 information

CS570: Introduction to Data Mining

CS570: Introduction to Data Mining CS570: Introduction to Data Mining Classification Advanced Reading: Chapter 8.4 & 8.5 Han, Chapters 4.5 & 4.6 Tan Anca Doloc-Mihu, Ph.D. Slides courtesy of Li Xiong, Ph.D., 2011 Han, Kamber & Pei. Data

More information

Contents. ACE Presentation. Comparison with existing frameworks. Technical aspects. ACE 2.0 and future work. 24 October 2009 ACE 2

Contents. ACE Presentation. Comparison with existing frameworks. Technical aspects. ACE 2.0 and future work. 24 October 2009 ACE 2 ACE Contents ACE Presentation Comparison with existing frameworks Technical aspects ACE 2.0 and future work 24 October 2009 ACE 2 ACE Presentation 24 October 2009 ACE 3 ACE Presentation Framework for using

More information

Study on Classifiers using Genetic Algorithm and Class based Rules Generation

Study on Classifiers using Genetic Algorithm and Class based Rules Generation 2012 International Conference on Software and Computer Applications (ICSCA 2012) IPCSIT vol. 41 (2012) (2012) IACSIT Press, Singapore Study on Classifiers using Genetic Algorithm and Class based Rules

More information

Data Mining Classification: Alternative Techniques. Imbalanced Class Problem

Data Mining Classification: Alternative Techniques. Imbalanced Class Problem Data Mining Classification: Alternative Techniques Imbalanced Class Problem Introduction to Data Mining, 2 nd Edition by Tan, Steinbach, Karpatne, Kumar Class Imbalance Problem Lots of classification problems

More information

CS249: ADVANCED DATA MINING

CS249: 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 information

A Comparison of Decision Tree Algorithms For UCI Repository Classification

A Comparison of Decision Tree Algorithms For UCI Repository Classification A Comparison of Decision Tree Algorithms For UCI Repository Classification Kittipol Wisaeng Mahasakham Business School (MBS), Mahasakham University Kantharawichai, Khamriang, Mahasarakham, 44150, Thailand.

More information

Best First and Greedy Search Based CFS and Naïve Bayes Algorithms for Hepatitis Diagnosis

Best First and Greedy Search Based CFS and Naïve Bayes Algorithms for Hepatitis Diagnosis Best First and Greedy Search Based CFS and Naïve Bayes Algorithms for Hepatitis Diagnosis CHAPTER 3 BEST FIRST AND GREEDY SEARCH BASED CFS AND NAÏVE BAYES ALGORITHMS FOR HEPATITIS DIAGNOSIS 3.1 Introduction

More information

Naïve 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 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 information

Data Mining. Practical Machine Learning Tools and Techniques. Slides for Chapter 5 of Data Mining by I. H. Witten, E. Frank and M. A.

Data Mining. Practical Machine Learning Tools and Techniques. Slides for Chapter 5 of Data Mining by I. H. Witten, E. Frank and M. A. Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 5 of Data Mining by I. H. Witten, E. Frank and M. A. Hall Credibility: Evaluating what s been learned Issues: training, testing,

More information

Evaluating Classifiers

Evaluating 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 information

IV B. Tech I semester (JNTUH-R13)

IV B. Tech I semester (JNTUH-R13) St. MARTIN s ENGINERING COLLEGE Dhulapally(V), Qutbullapur(M), Secunderabad-500014 COMPUTER SCIENCE AND ENGINEERING LAB MANUAL OF DATAWAREHOUSE AND DATAMINING IV B. Tech I semester (JNTUH-R13) Prepared

More information

INTRODUCTION TO MACHINE LEARNING. Measuring model performance or error

INTRODUCTION 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 information

Boolean Classification

Boolean Classification EE04 Spring 08 S. Lall and S. Boyd Boolean Classification Sanjay Lall and Stephen Boyd EE04 Stanford University Boolean classification Boolean classification I supervised learning is called boolean classification

More information

Data Mining: Classifier Evaluation. CSCI-B490 Seminar in Computer Science (Data Mining)

Data Mining: Classifier Evaluation. CSCI-B490 Seminar in Computer Science (Data Mining) Data Mining: Classifier Evaluation CSCI-B490 Seminar in Computer Science (Data Mining) Predictor Evaluation 1. Question: how good is our algorithm? how will we estimate its performance? 2. Question: what

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.. 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 information

Different Classification Technique for Data mining in Insurance Industry using weka

Different Classification Technique for Data mining in Insurance Industry using weka IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 19, Issue 1, Ver. I (Jan.-Feb. 2017), PP 11-18 www.iosrjournals.org Different Classification Technique for Data

More information

Application of the pessimistic pruning to increase the accuracy of C4.5 algorithm in diagnosing chronic kidney disease

Application of the pessimistic pruning to increase the accuracy of C4.5 algorithm in diagnosing chronic kidney disease Journal of Physics: Conference Series PAPER OPEN ACCESS Application of the pessimistic pruning to increase the accuracy of C4.5 algorithm in diagnosing chronic kidney disease To cite this article: M A

More information

Network Traffic Measurements and Analysis

Network 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 information

DATA 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 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 information

A Comparative Study of Selected Classification Algorithms of Data Mining

A Comparative Study of Selected Classification Algorithms of Data Mining Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 4, Issue. 6, June 2015, pg.220

More information

Machine learning in fmri

Machine learning in fmri Machine learning in fmri Validation Alexandre Savio, Maite Termenón, Manuel Graña 1 Computational Intelligence Group, University of the Basque Country December, 2010 1/18 Outline 1 Motivation The validation

More information

Machine Learning Techniques for Data Mining

Machine Learning Techniques for Data Mining Machine Learning Techniques for Data Mining Eibe Frank University of Waikato New Zealand 10/25/2000 1 PART V Credibility: Evaluating what s been learned 10/25/2000 2 Evaluation: the key to success How

More information

MEASURING CLASSIFIER PERFORMANCE

MEASURING CLASSIFIER PERFORMANCE MEASURING CLASSIFIER PERFORMANCE ERROR COUNTING Error types in a two-class problem False positives (type I error): True label is -1, predicted label is +1. False negative (type II error): True label is

More information

Metrics for Performance Evaluation How to evaluate the performance of a model? Methods for Performance Evaluation How to obtain reliable estimates?

Metrics 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 information

SWETHA ENGINEERING COLLEGE (Approved by AICTE, New Delhi, Affiliated to JNTUA) DATA MINING USING WEKA

SWETHA ENGINEERING COLLEGE (Approved by AICTE, New Delhi, Affiliated to JNTUA) DATA MINING USING WEKA SWETHA ENGINEERING COLLEGE (Approved by AICTE, New Delhi, Affiliated to JNTUA) DATA MINING USING WEKA LAB RECORD N99A49G70E68S51H Data Mining using WEKA 1 WEKA [ Waikato Environment for Knowledge Analysis

More information

Data Mining and Knowledge Discovery: Practice Notes

Data 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 information

DATA MINING LECTURE 9. Classification Decision Trees Evaluation

DATA MINING LECTURE 9. Classification Decision Trees Evaluation DATA MINING LECTURE 9 Classification Decision Trees Evaluation 10 10 Illustrating Classification Task Tid Attrib1 Attrib2 Attrib3 Class 1 Yes Large 125K No 2 No Medium 100K No 3 No Small 70K No 4 Yes Medium

More information

DATA MINING INTRODUCTION TO CLASSIFICATION USING LINEAR CLASSIFIERS

DATA 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 information

What is Learning? CS 343: Artificial Intelligence Machine Learning. Raymond J. Mooney. Problem Solving / Planning / Control.

What is Learning? CS 343: Artificial Intelligence Machine Learning. Raymond J. Mooney. Problem Solving / Planning / Control. What is Learning? CS 343: Artificial Intelligence Machine Learning Herbert Simon: Learning is any process by which a system improves performance from experience. What is the task? Classification Problem

More information

ECE 5470 Classification, Machine Learning, and Neural Network Review

ECE 5470 Classification, Machine Learning, and Neural Network Review ECE 5470 Classification, Machine Learning, and Neural Network Review Due December 1. Solution set Instructions: These questions are to be answered on this document which should be submitted to blackboard

More information

Data Mining Concepts & Techniques

Data 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 information

CREDIT 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 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 information

2. On classification and related tasks

2. On classification and related tasks 2. On classification and related tasks In this part of the course we take a concise bird s-eye view of different central tasks and concepts involved in machine learning and classification particularly.

More information

Classification. Instructor: Wei Ding

Classification. 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 information

NMLRG #4 meeting in Berlin. Mobile network state characterization and prediction. P.Demestichas (1), S. Vassaki (2,3), A.Georgakopoulos (2,3)

NMLRG #4 meeting in Berlin. Mobile network state characterization and prediction. P.Demestichas (1), S. Vassaki (2,3), A.Georgakopoulos (2,3) NMLRG #4 meeting in Berlin Mobile network state characterization and prediction P.Demestichas (1), S. Vassaki (2,3), A.Georgakopoulos (2,3) (1)University of Piraeus (2)WINGS ICT Solutions, www.wings-ict-solutions.eu/

More information

CSE Data Mining Concepts and Techniques STATISTICAL METHODS (REGRESSION) Professor- Anita Wasilewska. Team 13

CSE 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

INTRODUCTION TO DATA MINING. Daniel Rodríguez, University of Alcalá

INTRODUCTION TO DATA MINING. Daniel Rodríguez, University of Alcalá INTRODUCTION TO DATA MINING Daniel Rodríguez, University of Alcalá Outline Knowledge Discovery in Datasets Model Representation Types of models Supervised Unsupervised Evaluation (Acknowledgement: Jesús

More information

DESIGN AND EVALUATION OF MACHINE LEARNING MODELS WITH STATISTICAL FEATURES

DESIGN AND EVALUATION OF MACHINE LEARNING MODELS WITH STATISTICAL FEATURES EXPERIMENTAL WORK PART I CHAPTER 6 DESIGN AND EVALUATION OF MACHINE LEARNING MODELS WITH STATISTICAL FEATURES The evaluation of models built using statistical in conjunction with various feature subset

More information

Interpretation and evaluation

Interpretation and evaluation Interpretation and evaluation 1. Descriptive tasks Evaluation based on novelty, interestingness, usefulness and understandability Qualitative evaluation: obvious (common sense) knowledge knowledge that

More information

Machine Learning for. Artem Lind & Aleskandr Tkachenko

Machine 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 information

Extreme Learning Machines. Tony Oakden ANU AI Masters Project (early Presentation) 4/8/2014

Extreme Learning Machines. Tony Oakden ANU AI Masters Project (early Presentation) 4/8/2014 Extreme Learning Machines Tony Oakden ANU AI Masters Project (early Presentation) 4/8/2014 This presentation covers: Revision of Neural Network theory Introduction to Extreme Learning Machines ELM Early

More information

CISC 4631 Data Mining

CISC 4631 Data Mining CISC 4631 Data Mining Lecture 03: Introduction to classification Linear classifier Theses slides are based on the slides by Tan, Steinbach and Kumar (textbook authors) Eamonn Koegh (UC Riverside) 1 Classification:

More information

Basic Tokenizing, Indexing, and Implementation of Vector-Space Retrieval

Basic Tokenizing, Indexing, and Implementation of Vector-Space Retrieval Basic Tokenizing, Indexing, and Implementation of Vector-Space Retrieval 1 Naïve Implementation Convert all documents in collection D to tf-idf weighted vectors, d j, for keyword vocabulary V. Convert

More information

Information Management course

Information 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 information

Contents 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 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 information

ECLT 5810 Evaluation of Classification Quality

ECLT 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 information

Artificial Neural Networks (Feedforward Nets)

Artificial 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 information

Sergiu Nedevschi Computer Science Department Technical University of Cluj-Napoca

Sergiu Nedevschi Computer Science Department Technical University of Cluj-Napoca A comparative study of pedestrian detection methods using classical Haar and HoG features versus bag of words model computed from Haar and HoG features Raluca Brehar Computer Science Department Technical

More information

A Comparative Study of Locality Preserving Projection and Principle Component Analysis on Classification Performance Using Logistic Regression

A 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 information

GRIND Gene Regulation INference using Dual thresholding. Ir. W.P.A. Ligtenberg Eindhoven University of Technology

GRIND Gene Regulation INference using Dual thresholding. Ir. W.P.A. Ligtenberg Eindhoven University of Technology GRIND Gene Regulation INference using Dual thresholding Ir. W.P.A. Ligtenberg Eindhoven University of Technology Overview Project aim Introduction to network inference Benchmark (ValGRINT) Results GRIND

More information

Cross- Valida+on & ROC curve. Anna Helena Reali Costa PCS 5024

Cross- Valida+on & ROC curve. Anna Helena Reali Costa PCS 5024 Cross- Valida+on & ROC curve Anna Helena Reali Costa PCS 5024 Resampling Methods Involve repeatedly drawing samples from a training set and refibng a model on each sample. Used in model assessment (evalua+ng

More information

DATA MINING LECTURE 9. Classification Basic Concepts Decision Trees Evaluation

DATA 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 information

Decision trees. For this lab, we will use the Carseats data set from the ISLR package. (install and) load the package with the data set

Decision trees. For this lab, we will use the Carseats data set from the ISLR package. (install and) load the package with the data set Decision trees For this lab, we will use the Carseats data set from the ISLR package. (install and) load the package with the data set # install.packages('islr') library(islr) Carseats is a simulated data

More information

Evaluation Metrics. (Classifiers) CS229 Section Anand Avati

Evaluation 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 information

Data Mining and Knowledge Discovery Practice notes 2

Data 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 information

Pattern recognition (4)

Pattern recognition (4) Pattern recognition (4) 1 Things we have discussed until now Statistical pattern recognition Building simple classifiers Supervised classification Minimum distance classifier Bayesian classifier (1D and

More information

Data Mining and Knowledge Discovery: Practice Notes

Data 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 information

Classification and Regression

Classification 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 information

Machine Learning Methods. Majid Masso, PhD Bioinformatics and Computational Biology George Mason University

Machine Learning Methods. Majid Masso, PhD Bioinformatics and Computational Biology George Mason University Machine Learning Methods Majid Masso, PhD Bioinformatics and Computational Biology George Mason University Introductory Example Attributes X and Y measured for each person (example or instance) in a training

More information

Functions and Families

Functions and Families Unit 3 Functions and Families Name: Date: Hour: Function Transformations Notes PART 1 By the end of this lesson, you will be able to Describe horizontal translations and vertical stretches/shrinks of functions

More information

โครงการอบรมเช งปฏ บ ต การ น กว ทยาการข อม ลภาคร ฐ (Government Data Scientist)

โครงการอบรมเช งปฏ บ ต การ น กว ทยาการข อม ลภาคร ฐ (Government Data Scientist) โครงการอบรมเช งปฏ บ ต การ น กว ทยาการข อม ลภาคร ฐ (Government Data Scientist) (ว นท 2) สถาบ นพ ฒนาบ คลากรด านด จ ท ลภาคร ฐ สาน กงานพ ฒนาร ฐบาลด จ ท ล (องค การมหาชน) บรรยายโดย ผศ.ดร.โษฑศ ร ตต ธรรมบ ษด อาจารย

More information

Comparative Study of J48, Naive Bayes and One-R Classification Technique for Credit Card Fraud Detection using WEKA

Comparative Study of J48, Naive Bayes and One-R Classification Technique for Credit Card Fraud Detection using WEKA Advances in Computational Sciences and Technology ISSN 0973-6107 Volume 10, Number 6 (2017) pp. 1731-1743 Research India Publications http://www.ripublication.com Comparative Study of J48, Naive Bayes

More information

Advertisement Image Detection

Advertisement Image Detection Zhaoxi Li 0140068 Yu Wang 0140085 Bing Yue 0153373 Software Engineering and Computer Science 4/6TF3 Data Mining: Concepts and Algorithms Course Project Advertisement Image Detection Instructor: Dr. Jiming

More information

NETWORK FAULT DETECTION - A CASE FOR DATA MINING

NETWORK FAULT DETECTION - A CASE FOR DATA MINING NETWORK FAULT DETECTION - A CASE FOR DATA MINING Poonam Chaudhary & Vikram Singh Department of Computer Science Ch. Devi Lal University, Sirsa ABSTRACT: Parts of the general network fault management problem,

More information

International Journal of Computer Engineering and Applications, Volume XI, Issue XII, Dec. 17, ISSN

International Journal of Computer Engineering and Applications, Volume XI, Issue XII, Dec. 17,   ISSN RULE BASED CLASSIFICATION FOR NETWORK INTRUSION DETECTION SYSTEM USING USNW-NB 15 DATASET Dr C Manju Assistant Professor, Department of Computer Science Kanchi Mamunivar center for Post Graduate Studies,

More information

Data Mining D E C I S I O N T R E E. Matteo Golfarelli

Data Mining D E C I S I O N T R E E. Matteo Golfarelli Data Mining D E C I S I O N T R E E Matteo Golfarelli Decision Tree It is one of the most widely used classification techniques that allows you to represent a set of classification rules with a tree. Tree:

More information

Advanced Video Content Analysis and Video Compression (5LSH0), Module 8B

Advanced 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 information

Data Mining Classification: Bayesian Decision Theory

Data Mining Classification: Bayesian Decision Theory Data Mining Classification: Bayesian Decision Theory Lecture Notes for Chapter 2 R. O. Duda, P. E. Hart, and D. G. Stork, Pattern classification, 2nd ed. New York: Wiley, 2001. Lecture Notes for Chapter

More information

DATA MINING AND MACHINE LEARNING. Lecture 6: Data preprocessing and model selection Lecturer: Simone Scardapane

DATA MINING AND MACHINE LEARNING. Lecture 6: Data preprocessing and model selection Lecturer: Simone Scardapane DATA MINING AND MACHINE LEARNING Lecture 6: Data preprocessing and model selection Lecturer: Simone Scardapane Academic Year 2016/2017 Table of contents Data preprocessing Feature normalization Missing

More information

CS 584 Data Mining. Classification 3

CS 584 Data Mining. Classification 3 CS 584 Data Mining Classification 3 Today Model evaluation & related concepts Additional classifiers Naïve Bayes classifier Support Vector Machine Ensemble methods 2 Model Evaluation Metrics for Performance

More information

Nearest Neighbor 3D Segmentation with Context Features

Nearest Neighbor 3D Segmentation with Context Features Paper 10574-21 Session 4: Machine Learning, 3:30 PM - 5:30 PM, Salon B Nearest Neighbor 3D Segmentation with Context Features Evelin Hristova, Heinrich Schulz, Tom Brosch, Mattias P. Heinrich, Hannes Nickisch

More information

Machine Learning Software ROOT/TMVA

Machine Learning Software ROOT/TMVA Machine Learning Software ROOT/TMVA LIP Data Science School / 12-14 March 2018 ROOT ROOT is a software toolkit which provides building blocks for: Data processing Data analysis Data visualisation Data

More information

Probabilistic Classifiers DWML, /27

Probabilistic 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 information

Use of Synthetic Data in Testing Administrative Records Systems

Use of Synthetic Data in Testing Administrative Records Systems Use of Synthetic Data in Testing Administrative Records Systems K. Bradley Paxton and Thomas Hager ADI, LLC 200 Canal View Boulevard, Rochester, NY 14623 brad.paxton@adillc.net, tom.hager@adillc.net Executive

More information

Dr. Prof. El-Bahlul Emhemed Fgee Supervisor, Computer Department, Libyan Academy, Libya

Dr. Prof. El-Bahlul Emhemed Fgee Supervisor, Computer Department, Libyan Academy, Libya Volume 5, Issue 1, January 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Performance

More information

A Critical Study of Selected Classification Algorithms for Liver Disease Diagnosis

A Critical Study of Selected Classification Algorithms for Liver Disease Diagnosis A Critical Study of Selected Classification s for Liver Disease Diagnosis Shapla Rani Ghosh 1, Sajjad Waheed (PhD) 2 1 MSc student (ICT), 2 Associate Professor (ICT) 1,2 Department of Information and Communication

More information

DEEP LEARNING OF COMPRESSED SENSING OPERATORS WITH STRUCTURAL SIMILARITY (SSIM) LOSS

DEEP LEARNING OF COMPRESSED SENSING OPERATORS WITH STRUCTURAL SIMILARITY (SSIM) LOSS DEEP LEARNING OF COMPRESSED SENSING OPERATORS WITH STRUCTURAL SIMILARITY (SSIM) LOSS ABSTRACT Compressed sensing (CS) is a signal processing framework for efficiently reconstructing a signal from a small

More information

Math 1314 Test 3 Review Material covered is from Lessons 9 15

Math 1314 Test 3 Review Material covered is from Lessons 9 15 Math 1314 Test 3 Review Material covered is from Lessons 9 15 1. The total weekly cost of manufacturing x cameras is given by the cost function: 3 2 Cx ( ) 0.0001x 0.4x 800x 3, 000. Use the marginal cost

More information

CLASSIFICATION WITH RADIAL BASIS AND PROBABILISTIC NEURAL NETWORKS

CLASSIFICATION 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 information

Evaluation of different biological data and computational classification methods for use in protein interaction prediction.

Evaluation of different biological data and computational classification methods for use in protein interaction prediction. 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 Motivation Correctly

More information