Waleed Pervaiz CSE 352
|
|
|
- Ellen Lane
- 5 years ago
- Views:
Transcription
1 Waleed Pervaiz CSE 352
2 Classification Tool: WEKA Waikato Environment for Knowledge Analysis by The University of Waikato. Available on the internet at:
3 The Raw data does give us a lot of information. However, in this form most of this information is useless and doesn t tell us anything.
4 To prepare data for pre processing the following steps were taken. Any attributes that have missing data (i.e. more than 20%) will be removed. The following attributes were thus removed: Pb As Cd Ni Sc Co Li Mo
5 All other attributes that are missing values were filled in with their averages (mean). Missing values for the following attributes were inserted: TiO2 (Carbonates) Mean: (Inserted at E58 and E62) P2O5 (Carbonates) Mean: 0.74 (Inserted at L33) S (Carbonates) Mean: 423 (Inserted at M52, M66, M75) Zn (Carbonates) Mean: 16 (Inserted at N46, N53, N67) Cu (Carbonates) Mean: 3 (Inserted at O37, O43, O46, O48, O52, O55, O57, O60, O63, O67) Cr (Galene) Mean: 9.6 (Inserted at P85, P87) Cr (Spahlerite) Mean: 3.6 (Inserted at P90) V (Carbonates) Mean: 5.2 (Inserted at Q43, Q48, Q51, Q52, Q60, Q62, Q66, Q71, Q75) V (Galene) Mean: 2.5 (Inserted at Q86) V (Spahlerite) Mean: 9.4 (Inserted at Q90)
6 For easier reading, class values were replaced with simpler values. The following values were changed: R. carbonatées changed to C1 Pyrite changed to C2 Chalcopyrites changed to C3 Galène changed to C4 Spahlerite changed to C5 Sédiments terrigènes changed to C6
7 Using WEKA, we remove any noisy data that may unnecessarily skew our data and results.
8 With all the missing data filled in, the noisy data eliminated, we discretize the data using the WEKA tool. (3 equal frequency bins)
9 Values in the bins were then replaced by specific words: Low Medium High This helps in understanding data better. Decision Tree algorithms will still work with these non numerical values.
10 The following experiments will be carried out on our data: Full Learning: Construction of decision trees characterizing all classes. Contrast Learning: Using all attributes to compare class C1 with the rest of the classes. Limited Learning: Construction of decision tree using only the major attributes.
11 Experiment 1: Full Learning Decision Trees were generated using the J48 algorithm.
12
13 We got the following discriminant rules. IF Ta= Low AND Lu= Low AND Fe2O3= Low THEN Class= C1 IF Ta= Low AND Lu= Low AND Fe2O3= Medium THEN Class= C1 IF Ta= Low AND Lu= Low AND Fe2O3= High AND Cu= Low THEN Class= C1 IF Ta= Low AND Lu= Low AND Fe2O3= High AND Cu= Medium THEN Class= C2 IF Ta= Low AND Lu= Low AND Fe2O3= High AND Cu= High AND Cr= Low THEN Class= C1 IF Ta= Low AND Lu= Low AND Fe2O3= High AND Cu= High AND Cr= Medium THEN Class= C1 IF Ta= Low AND Lu= Low AND Fe2O3= High AND Cu= High AND Cr= High THEN Class= C4
14 We got the following discriminant rules. IF Ta= Low AND Lu= Medium THEN Class= C1 IF Ta= Low AND Lu= High THEN Class= C1 IF Ta= Medium AND CaO= High THEN Class= C1 IF Ta= Medium AND CaO= Medium THEN Class= C1 IF Ta= Medium AND CaO= Low THEN Class= C2 IF Ta= High AND Zn= Low THEN Class= C1 IF Ta= High AND Zn= Medium THEN Class= C6 IF Ta= High AND Zn= High THEN Class= C6 Predictive Accuracy Determined: 70.58%
15 Experiment 2: Contrast Learning Decision Trees were generated using the J48 algorithm.
16
17 We got the following discriminant rules. IF Fe2O3= Low THEN Class= C1 IF Fe2O3= Medium THEN Class= C1 IF Fe2O3= High AND CaO= High THEN Class= C1 IF Fe2O3= High AND CaO= Medium AND Rb= Medium THEN Class= NOT C1 IF Fe2O3= High AND CaO= Medium AND Rb= Low THEN Class= C1 IF Fe2O3= High AND CaO= Medium AND Rb= High THEN Class= C1 IF Fe2O3= High AND CaO= Low AND Zn= Low THEN Class= C1
18 We got the following discriminant rules. IF Fe2O3= High AND CaO= Low AND Zn= Medium THEN Class= NOT C1 IF Fe2O3= High AND CaO= Low AND Zn= High THEN Class= NOT C1 Predictive Accuracy Determined: 97.06%
19 Experiment 3: Using Major Attributes Decision Trees were generated using the J48 algorithm.
20
21 We got the following discriminant rules. IF Fe2O3= Low THEN Class= C1 IF Fe2O3= Medium THEN Class= C1 IF Fe2O3= High AND CaO= High THEN Class= C1 IF Fe2O3= High AND CaO= Medium THEN Class= C1 IF Fe2O3= High AND CaO= Low AND Zn= Low THEN Class= C1 IF Fe2O3= High AND CaO= Low AND Zn= Medium AND S= Low THEN Class= C6 IF Fe2O3= High AND CaO= Low AND Zn= Medium AND S= Medium THEN Class= C6
22 We got the following discriminant rules. IF Fe2O3= High AND CaO= Low AND Zn= Medium AND S= High THEN Class= C2 IF Fe2O3= High AND CaO= Low AND Zn= High THEN Class= C6 Predictive Accuracy Determined: 82.35%
23 With all the missing data filled in, the noisy data eliminated, we use another method of data discretization. (4 equal width bins)
24 The following experiments will be carried out on our data: Full Learning: Construction of decision trees characterizing all classes. Contrast Learning: Using all attributes to compare class C1 with the rest of the classes. Limited Learning: Construction of decision tree using only the major attributes.
25 Experiment 1: Full Learning Decision Trees were generated using the J48 algorithm.
26
27 We got the following discriminant rules. IF Ta= Low AND Lu= Low AND Fe2O3= Low THEN Class= C1 IF Ta= Low AND Lu= Low AND Fe2O3= Medium THEN Class= C1 IF Ta= Low AND Lu= Low AND Fe2O3= High AND Cu= Low THEN Class= C1 IF Ta= Low AND Lu= Low AND Fe2O3= High AND Cu= Medium THEN Class= C2 IF Ta= Low AND Lu= Low AND Fe2O3= High AND Cu= High AND Cr= Low THEN Class= C1 IF Ta= Low AND Lu= Low AND Fe2O3= High AND Cu= High AND Cr= Medium THEN Class= C1 IF Ta= Low AND Lu= Low AND Fe2O3= High AND Cu= High AND Cr= High THEN Class= C4
28 We got the following discriminant rules. IF Ta= Low AND Lu= Medium THEN Class= C1 IF Ta= Low AND Lu= High THEN Class= C1 IF Ta= Medium AND CaO= High THEN Class= C1 IF Ta= Medium AND CaO= Medium THEN Class= C1 IF Ta= Medium AND CaO= Low THEN Class= C2 IF Ta= High AND Zn= Low THEN Class= C1 IF Ta= High AND Zn= Medium THEN Class= C6 IF Ta= High AND Zn= High THEN Class= C6 Predictive Accuracy Determined: 79.59%
29 Experiment 2: Contrast Learning Decision Trees were generated using the J48 algorithm.
30
31 We got the following discriminant rules. IF Fe2O3= Low THEN Class= C1 IF Fe2O3= Medium THEN Class= C1 IF Fe2O3= High AND CaO= High THEN Class= C1 IF Fe2O3= High AND CaO= Medium AND Rb= Medium THEN Class= NOT C1 IF Fe2O3= High AND CaO= Medium AND Rb= Low THEN Class= C1 IF Fe2O3= High AND CaO= Medium AND Rb= High THEN Class= C1 IF Fe2O3= High AND CaO= Low AND Zn= Low THEN Class= C1
32 We got the following discriminant rules. IF Fe2O3= High AND CaO= Low AND Zn= Medium AND Tb= Low THEN Class= NOT C1 IF Fe2O3= High AND CaO= Low AND Zn= Medium AND Tb= Medium THEN Class= C1 IF Fe2O3= High AND CaO= Low AND Zn= Medium AND Tb= High THEN Class= NOT C1 IF Fe2O3= High AND CaO= Low AND Zn= High THEN Class= NOT C1 Predictive Accuracy Determined: 89.79%
33 Experiment 3: Using Major Attributes Decision Trees were generated using the J48 algorithm.
34
35 We got the following discriminant rules. IF Fe2O3= Low THEN Class= C1 IF Fe2O3= Medium THEN Class= C1 IF Fe2O3= High AND CaO= High THEN Class= C1 IF Fe2O3= High AND CaO= Medium THEN Class= C1 IF Fe2O3= High AND CaO= Low AND Zn= Low THEN Class= C1 IF Fe2O3= High AND CaO= Low AND Zn= Medium THEN Class= NOT C1 IF Fe2O3= High AND CaO= Low AND Zn= High THEN Class= NOT C1
36 Predictive Accuracy Determined: 94.90%
37 Here is a comparison of the accuracy achieved with each Dataset. Dataset #1 Dataset #2 Experiment % 79.59% Experiment % 89.79% Experiment % 94.90% Dataset 1 was carried out using 3 bins equal frequency discretization. Dataset 2 was carried out using 4 bins equal width discretization.
38 High accuracy for a particular value can sometimes be misleading since there is a lot of data (77 records) for C1 as compared to data (21 records) for other classes. WEKA produces different rules depending on the techniques used for data preparation. Dataset 2 generally had better accuracy. Thus, we can conclude that the 4 bin equal width method was slightly more accurate than the 3 bin equal frequency method. Comparing classes with each other gave the best overall accuracy. (i.e. comparing C1 with all other classes)
GEOPT19 - AN INTERNATIONAL PROFICIENCY TEST FOR ANALYTICAL GEOCHEMISTRY LABORATORIES REPORT ON ROUND 19 / July 2006 (Gabbro, MGR-N)
GEOPT19 - AN INTERNATIONAL PROFICIENCY TEST FOR ANALYTICAL GEOCHEMISTRY LABORATORIES REPORT ON ROUND 19 / July 2006 (Gabbro, MGR-N) Peter C. Webb 1 *, Michael Thompson 2, Philip J. Potts 1 and B. Batjargal
GeoPT22 - MBL-1, Basalt
GeoPT22 - MBL-1, Basalt Veranstalter: International Association of Geoanalysts and Geostandards Newsletter - GeoPT22 Ringversuchsmaterial: MBL-1, Basalt RV geschlossen: 2008 2 Literatur: Proficiency Testing
GEOPT10 - AN INTERNATIONAL PROFICIENCY TEST FOR ANALYTICAL GEOCHEMISTRY LABORATORIES - REPORT ON ROUND 10 / December 2001 (CH-1 marine sediment).
GEOPT10 - AN INTERNATIONAL PROFICIENCY TEST FOR ANALYTICAL GEOCHEMISTRY LABORATORIES - REPORT ON ROUND 10 / December 2001 (CH-1 marine sediment). Philip J. Potts 1 *, Michael Thompson 2, Peter C. Webb
ENDF/B-VII.1 versus ENDFB/-VII.0: What s Different?
LLNL-TR-548633 ENDF/B-VII.1 versus ENDFB/-VII.0: What s Different? by Dermott E. Cullen Lawrence Livermore National Laboratory P.O. Box 808/L-198 Livermore, CA 94550 March 17, 2012 Approved for public
Chapter 8 The C 4.5*stat algorithm
109 The C 4.5*stat algorithm This chapter explains a new algorithm namely C 4.5*stat for numeric data sets. It is a variant of the C 4.5 algorithm and it uses variance instead of information gain for the
REE-mineralogy of Suhuvaara appinite, Inari, Northern Finland Thair Al-Ani and Olli Sarapää
Northern Finland Unit M42/2010/67 22.2.2011 Rovaniemi REE-mineralogy of Suhuvaara appinite, Inari, Northern Finland Thair Al-Ani and Olli Sarapää GEOLOGICAL SURVEY OF FINLAND DOCUMENTATION PAGE Date /
Activity concentration for exempt material (Bq/g)
173.436 Exempt material activity concentrations and exempt consignment activity limits for radionuclides. The Table of Exempt material activity concentrations and exempt consignment activity limits for
Quality of Semiconductor Raw Materials: Evolution and Challenges. Yongqiang Lu Kevin McLaughlin
Quality of Semiconductor Raw Materials: Evolution and Challenges Yongqiang Lu Kevin McLaughlin Outline Advance of Fab technologies and the evolution of raw materials for ever higher quality Challenges:
Supporting Information. Highly Selective and Reversible Chemosensor for Pd 2+ Detected by Fluorescence, Colorimetry and Test Paper.
Supporting Information Highly Selective and Reversible Chemosensor for Pd 2+ Detected by Fluorescence, Colorimetry and Test Paper Mian Wang, Xiaomei Liu, Huizhe Lu, Hongmei Wang* and Zhaohai Qin* Department
AN INTERNATIONAL PROFICIENCY TEST FOR ANALYTICAL GEOCHEMISTRY LABORATORIES REPORT ON ROUND
GeoPT23 AN INTERNATIONAL PROFICIENCY TEST FOR ANALYTICAL GEOCHEMISTRY LABORATORIES REPORT ON ROUND 23 (Separation Lake pegmatite, OU-9) and 23A (Manganese nodule, FeMn-1) / August 2008 Peter C. Webb 1
Row 1 This is data This is data
mpdf TABLES CSS Styles The CSS properties for tables and cells is increased over that in html2fpdf. It includes recognition of THEAD, TFOOT and TH. See below for other facilities such as autosizing, and
Row 1 This is data This is data. This is data out of p This is bold data p This is bold data out of p This is normal data after br H3 in a table
mpdf TABLES CSS Styles The CSS properties for tables and cells is increased over that in html2fpdf. It includes recognition of THEAD, TFOOT and TH. See below for other facilities such as autosizing, and
GeoPT14 - OshBO, Alkaline Granite
GeoPT - OshBO, Alkaline Granite Veranstalter: International Association of Geoanalysts and Geostandards Newsletter - GeoPT Ringversuchsmaterial: OshBO, Alkaline Granite RV geschlossen: 0 Literatur: Proficiency
Data Preparation. UROŠ KRČADINAC URL:
Data Preparation UROŠ KRČADINAC EMAIL: uros@krcadinac.com URL: http://krcadinac.com Normalization Normalization is the process of rescaling the values to a specific value scale (typically 0-1) Standardization
Paul's Online Math Notes Calculus III (Notes) / Applications of Partial Derivatives / Lagrange Multipliers Problems][Assignment Problems]
![Paul's Online Math Notes Calculus III (Notes) / Applications of Partial Derivatives / Lagrange Multipliers Problems][Assignment Problems]](/thumbs/77/75726948.jpg "Paul's Online Math Notes Calculus III (Notes) / Applications of Partial Derivatives / Lagrange Multipliers Problems][Assignment Problems]")
1 of 9 25/04/2016 13:15 Paul's Online Math Notes Calculus III (Notes) / Applications of Partial Derivatives / Lagrange Multipliers Problems][Assignment Problems] [Notes] [Practice Calculus III - Notes
ISO INTERNATIONAL STANDARD. Water quality Digestion for the determination of selected elements in water Part 2: Nitric acid digestion
INTERNATIONAL STANDARD ISO 15587-2 First edition 2002-03-01 Water quality Digestion for the determination of selected elements in water Part 2: Nitric acid digestion Qualité de l'eau Digestion pour la
EE368 Project Report CD Cover Recognition Using Modified SIFT Algorithm
EE368 Project Report CD Cover Recognition Using Modified SIFT Algorithm Group 1: Mina A. Makar Stanford University mamakar@stanford.edu Abstract In this report, we investigate the application of the Scale-Invariant
NRF Filmetrics F40 SOP Revision /02/12 Page 1 of 5. Filmetrics F40 SOP
Page 1 of 5 Filmetrics F40 SOP The F40 is a reflectometer used to measure the thickness and optical constants of thin, transparent/semi-transparent films. By fitting it to a microscope the tool can have
Classification 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
Data Preparation. Nikola Milikić. Jelena Jovanović
Data Preparation Nikola Milikić nikola.milikic@fon.bg.ac.rs Jelena Jovanović jeljov@fon.bg.ac.rs Normalization Normalization is the process of rescaling the values of an attribute to a specific value range,
CSE 446 Bias-Variance & Naïve Bayes
CSE 446 Bias-Variance & Naïve Bayes Administrative Homework 1 due next week on Friday Good to finish early Homework 2 is out on Monday Check the course calendar Start early (midterm is right before Homework
CPSC 340: Machine Learning and Data Mining. Probabilistic Classification Fall 2017
CPSC 340: Machine Learning and Data Mining Probabilistic Classification Fall 2017 Admin Assignment 0 is due tonight: you should be almost done. 1 late day to hand it in Monday, 2 late days for Wednesday.
Hands on Datamining & Machine Learning with Weka
Step1: Click the Experimenter button to launch the Weka Experimenter. The Weka Experimenter allows you to design your own experiments of running algorithms on datasets, run the experiments and analyze
3 Virtual attribute subsetting
3 Virtual attribute subsetting Portions of this chapter were previously presented at the 19 th Australian Joint Conference on Artificial Intelligence (Horton et al., 2006). Virtual attribute subsetting
Preprocessing Short Lecture Notes cse352. Professor Anita Wasilewska
Preprocessing Short Lecture Notes cse352 Professor Anita Wasilewska Data Preprocessing Why preprocess the data? Data cleaning Data integration and transformation Data reduction Discretization and concept
GEOPT13 - AN INTERNATIONAL PROFICIENCY TEST FOR ANALYTICAL GEOCHEMISTRY LABORATORIES - REPORT ON ROUND 13 / July 2003 (Köln Loess)
GEOPT13 - AN INTERNATIONAL PROFICIENCY TEST FOR ANALYTICAL GEOCHEMISTRY LABORATORIES - REPORT ON ROUND 13 / July 2003 (Köln Loess) Philip J. Potts 1 *, Michael Thompson 2, Simon R.N. Chenery 3, Peter C.
A Fast Personal Palm print Authentication based on 3D-Multi Wavelet Transformation
A Fast Personal Palm print Authentication based on 3D-Multi Wavelet Transformation * A. H. M. Al-Helali, * W. A. Mahmmoud, and * H. A. Ali * Al- Isra Private University Email: adnan_hadi@yahoo.com Abstract:
Practical Data Mining COMP-321B. Tutorial 4: Preprocessing
Practical Data Mining COMP-321B Tutorial 4: Preprocessing Shevaun Ryan Mark Hall June 30, 2008 c 2006 University of Waikato 1 Introduction For this tutorial we will be using the Preprocess panel, the Classify
A Voxel-Based Skewness and Kurtosis Balancing Algorithm for Updating Road Networks from Airborne Lidar Data
A Voxel-Based Skewness and Kurtosis Balancing Algorithm for Updating Road Networks from Airborne Lidar Data Li Liu & Samsung Lim School of Civil & Environmental Engineering University of New South Wales
Project Management with RStudio
Project Management with RStudio Tzu L. Phang 2016-10-10 Project Management with RStudio: Overview Goto Class link to follow along Questions How can I manage my projects in R? Objectives To be able to create
ICP-OES. By: Dr. Sarhan A. Salman
ICP-OES By: Dr. Sarhan A. Salman AGENDA -- ICP-OES Overview - Module removal Optical components and gas control overview Module replacement --Continue module replacement Manual optical alignment --Precise
Shorthand for values: variables
Chapter 2 Shorthand for values: variables 2.1 Defining a variable You ve typed a lot of expressions into the computer involving pictures, but every time you need a different picture, you ve needed to find
So il Geochem ica l Ba seline and Env ironm en ta l Background Va lues of Agr icultura l Reg ion s in Zhejiang Prov ince.
2007, 23 (2) : 81-88 Journal of Ecology and Rural Environm ent 1, 1, 2, 1 (1., 311203;, 065000) 2. :,, 13,,, 52(), : ; ; ; : P632; X8; P59 : A: 1673-4831 (2007) 02-0081 - 08 So il Geochem ica l Ba seline
Data Mining. Lab 1: Data sets: characteristics, formats, repositories Introduction to Weka. I. Data sets. I.1. Data sets characteristics and formats
Data Mining Lab 1: Data sets: characteristics, formats, repositories Introduction to Weka I. Data sets I.1. Data sets characteristics and formats The data to be processed can be structured (e.g. data matrix,
CHAPTER 3. Preprocessing and Feature Extraction. Techniques
CHAPTER 3 Preprocessing and Feature Extraction Techniques CHAPTER 3 Preprocessing and Feature Extraction Techniques 3.1 Need for Preprocessing and Feature Extraction schemes for Pattern Recognition and
3D Face and Hand Tracking for American Sign Language Recognition
3D Face and Hand Tracking for American Sign Language Recognition NSF-ITR (2004-2008) D. Metaxas, A. Elgammal, V. Pavlovic (Rutgers Univ.) C. Neidle (Boston Univ.) C. Vogler (Gallaudet) The need for automated
Attribute Discretization and Selection. Clustering. NIKOLA MILIKIĆ UROŠ KRČADINAC
Attribute Discretization and Selection Clustering NIKOLA MILIKIĆ nikola.milikic@fon.bg.ac.rs UROŠ KRČADINAC uros@krcadinac.com Naive Bayes Features Intended primarily for the work with nominal attributes
OCR and OCV. Tom Brennan Artemis Vision Artemis Vision 781 Vallejo St Denver, CO (303)
OCR and OCV Tom Brennan Artemis Vision Artemis Vision 781 Vallejo St Denver, CO 80204 (303)832-1111 tbrennan@artemisvision.com www.artemisvision.com About Us Machine Vision Integrator Turnkey Systems OEM
Input: Concepts, Instances, Attributes
Input: Concepts, Instances, Attributes 1 Terminology Components of the input: Concepts: kinds of things that can be learned aim: intelligible and operational concept description Instances: the individual,
Domestic electricity consumption analysis using data mining techniques
Domestic electricity consumption analysis using data mining techniques Prof.S.S.Darbastwar Assistant professor, Department of computer science and engineering, Dkte society s textile and engineering institute,
Excel Basics Rice Digital Media Commons Guide Written for Microsoft Excel 2010 Windows Edition by Eric Miller
Excel Basics Rice Digital Media Commons Guide Written for Microsoft Excel 2010 Windows Edition by Eric Miller Table of Contents Introduction!... 1 Part 1: Entering Data!... 2 1.a: Typing!... 2 1.b: Editing
LA ICP MS Analysis with
LA ICP MS Analysis with V 1.0.4 04.06.2008 Software written by Murray Allan at Die Eidgenössische Technische Hochschule Zürich, CH and the University of Leeds, UK Additional Software writing: Dimitri Meier
Computing a Gain Chart. Comparing the computation time of data mining tools on a large dataset under Linux.
1 Introduction Computing a Gain Chart. Comparing the computation time of data mining tools on a large dataset under Linux. The gain chart is an alternative to confusion matrix for the evaluation of a classifier.
Machine Learning Techniques for Data Mining
Machine Learning Techniques for Data Mining Eibe Frank University of Waikato New Zealand 10/25/2000 1 PART VII Moving on: Engineering the input and output 10/25/2000 2 Applying a learner is not all Already
K*: An Instance-based Learner Using an Entropic Distance Measure
K*: An Instance-based Learner Using an Entropic Distance Measure John G. Cleary, Leonard E. Trigg, Dept. of Computer Science, University of Waikato, New Zealand. e-mail:{jcleary,trigg}@waikato.ac.nz Abstract
Pouya Kousha Fall 2018 CSE 5194 Prof. DK Panda
Pouya Kousha Fall 2018 CSE 5194 Prof. DK Panda 1 Observe novel applicability of DL techniques in Big Data Analytics. Applications of DL techniques for common Big Data Analytics problems. Semantic indexing
CSE 559A: Computer Vision
CSE 559A: Computer Vision Fall 2018: T-R: 11:30-1pm @ Lopata 101 Instructor: Ayan Chakrabarti (ayan@wustl.edu). Course Staff: Zhihao Xia, Charlie Wu, Han Liu http://www.cse.wustl.edu/~ayan/courses/cse559a/
Statistics Lecture 6. Looking at data one variable
Statistics 111 - Lecture 6 Looking at data one variable Chapter 1.1 Moore, McCabe and Craig Probability vs. Statistics Probability 1. We know the distribution of the random variable (Normal, Binomial)
COMP s1 - Getting started with the Weka Machine Learning Toolkit
COMP9417 16s1 - Getting started with the Weka Machine Learning Toolkit Last revision: Thu Mar 16 2016 1 Aims This introduction is the starting point for Assignment 1, which requires the use of the Weka
Singular Value Decomposition, and Application to Recommender Systems
Singular Value Decomposition, and Application to Recommender Systems CSE 6363 Machine Learning Vassilis Athitsos Computer Science and Engineering Department University of Texas at Arlington 1 Recommendation
Michele Samorani. University of Alberta School of Business. More tutorials are available on Youtube
Dataconda Tutorial* Michele Samorani University of Alberta School of Business More tutorials are available on Youtube What is Dataconda? Software program to generate a mining table from a relational database
APPENDIX C. Chain of Custody Forms
APPENDX C Chain of Custody Forms CHAN OF CUSTODY Servkes AE,rpo OWdL 1317 South 13th Ave. Kelso, WA 98626 (360> 5777222 (800) 6957222x07 FAX (360) 6361068 SR#: PAGE _ OF 000# PROJECT NAME,'r j PROJECTNMBER
More Learning. Ensembles Bayes Rule Neural Nets K-means Clustering EM Clustering WEKA
More Learning Ensembles Bayes Rule Neural Nets K-means Clustering EM Clustering WEKA 1 Ensembles An ensemble is a set of classifiers whose combined results give the final decision. test feature vector
TUBE: Command Line Program Calls
TUBE: Command Line Program Calls March 15, 2009 Contents 1 Command Line Program Calls 1 2 Program Calls Used in Application Discretization 2 2.1 Drawing Histograms........................ 2 2.2 Discretizing.............................
Report: Privacy-Preserving Classification on Deep Neural Network
Report: Privacy-Preserving Classification on Deep Neural Network Janno Veeorg Supervised by Helger Lipmaa and Raul Vicente Zafra May 25, 2017 1 Introduction In this report we consider following task: how
Three-Dimensional Motion Tracking using Clustering
Three-Dimensional Motion Tracking using Clustering Andrew Zastovnik and Ryan Shiroma Dec 11, 2015 Abstract Tracking the position of an object in three dimensional space is a fascinating problem with many
Decision 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
Team Members: Yingda Hu (yhx640), Brian Zhan (bjz002) James He (jzh642), Kevin Cheng (klc954)
Machine Learning Final Report EECS 349 Spring 2016 Instructor: Doug Downey Project Goal: The goal of our project is to predict the opening week box office revenue of a movie based upon features found from
Chapter 2 - Graphical Summaries of Data
Chapter 2 - Graphical Summaries of Data Data recorded in the sequence in which they are collected and before they are processed or ranked are called raw data. Raw data is often difficult to make sense
MULTI ORIENTATION PERFORMANCE OF FEATURE EXTRACTION FOR HUMAN HEAD RECOGNITION
MULTI ORIENTATION PERFORMANCE OF FEATURE EXTRACTION FOR HUMAN HEAD RECOGNITION Panca Mudjirahardjo, Rahmadwati, Nanang Sulistiyanto and R. Arief Setyawan Department of Electrical Engineering, Faculty of
3 Graphical Displays of Data
3 Graphical Displays of Data Reading: SW Chapter 2, Sections 1-6 Summarizing and Displaying Qualitative Data The data below are from a study of thyroid cancer, using NMTR data. The investigators looked
Motion Interpretation and Synthesis by ICA
Motion Interpretation and Synthesis by ICA Renqiang Min Department of Computer Science, University of Toronto, 1 King s College Road, Toronto, ON M5S3G4, Canada Abstract. It is known that high-dimensional
Organizing and Summarizing Data
Section 2.2 9 Organizing and Summarizing Data Section 2.2 C H A P T E R 2 4 Example 2 (pg. 72) A Histogram for Discrete Data To create a histogram, you have two choices: 1): enter all the individual data
3 Graphical Displays of Data
3 Graphical Displays of Data Reading: SW Chapter 2, Sections 1-6 Summarizing and Displaying Qualitative Data The data below are from a study of thyroid cancer, using NMTR data. The investigators looked
15-780: Graduate Artificial Intelligence. Decision trees
15-780: Graduate Artificial Intelligence Decision trees Graphical models So far we discussed models that capture joint probability distributions These have many uses, and can also be used to determine
Usability Test Report: Bento results interface 1
Usability Test Report: Bento results interface 1 Summary Emily Daly and Ian Sloat conducted usability testing on the functionality of the Bento results interface. The test was conducted at the temporary
A Brief Overview of Audio Information Retrieval. Unjung Nam CCRMA Stanford University
A Brief Overview of Audio Information Retrieval Unjung Nam CCRMA Stanford University 1 Outline What is AIR? Motivation Related Field of Research Elements of AIR Experiments and discussion Music Classification
Separating Speech From Noise Challenge
Separating Speech From Noise Challenge We have used the data from the PASCAL CHiME challenge with the goal of training a Support Vector Machine (SVM) to estimate a noise mask that labels time-frames/frequency-bins
CS 398 ACC Data Sourcing / Cleaning
CS 398 ACC Data Sourcing / Cleaning Prof. Robert J. Brunner Ben Congdon Tyler Kim MP6 How s it going? Due March 13th at 11:55 pm. Submit your results as a PDF report on Moodle Final Project Reminders Project
Hebei University of Technology A Text-Mining-based Patent Analysis in Product Innovative Process
A Text-Mining-based Patent Analysis in Product Innovative Process Liang Yanhong, Tan Runhua Abstract Hebei University of Technology Patent documents contain important technical knowledge and research results.
NOTES TO CONSIDER BEFORE ATTEMPTING EX 1A TYPES OF DATA
NOTES TO CONSIDER BEFORE ATTEMPTING EX 1A TYPES OF DATA Statistics is concerned with scientific methods of collecting, recording, organising, summarising, presenting and analysing data from which future
Learning from Data: Adaptive Basis Functions
Learning from Data: Adaptive Basis Functions November 21, 2005 http://www.anc.ed.ac.uk/ amos/lfd/ Neural Networks Hidden to output layer - a linear parameter model But adapt the features of the model.
DEFINITIVE ANNUAL REPORT Trace elements 2014
EXPERTISE, SERVICE PROVISION AND CUSTOMER RELATIONS QUALITY OF MEDICAL LABORATORIES CLINICAL BIOLOGY COMMISSION COMMITTEE OF EXPERTS EXTERNAL QUALITY ASSESSMENT IN CLINICAL BIOLOGY DEFINITIVE ANNUAL REPORT
Data Mining. Part 2. Data Understanding and Preparation. 2.4 Data Transformation. Spring Instructor: Dr. Masoud Yaghini. Data Transformation
Data Mining Part 2. Data Understanding and Preparation 2.4 Spring 2010 Instructor: Dr. Masoud Yaghini Outline Introduction Normalization Attribute Construction Aggregation Attribute Subset Selection Discretization
This guide will walk you through the steps for installing and using wget on Windows.
Wget Windows Guide This guide will walk you through the steps for installing and using wget on Windows. The Eye is currently sponsored by 10gbps.io. Check out their services, they re awesome. :) Quick
Computer Science 61 Scribe Notes Tuesday, November 25, 2014 (aka the day before Thanksgiving Break)
Computer Science 61 Scribe Notes Tuesday, November 25, 2014 (aka the day before Thanksgiving Break) Problem Set 6 Released! People have fun with it Make Games Snake Game Hack JavaScript Due Wed., last
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
Module: Rasters. 8.1 Lesson: Working with Raster Data Follow along: Loading Raster Data CHAPTER 8
CHAPTER 8 Module: Rasters We ve used rasters for digitizing before, but raster data can also be used directly. In this module, you ll see how it s done in QGIS. 8.1 Lesson: Working with Raster Data Raster
Waleed Pervaiz CSE 352
Waleed Pervaiz CSE 352 Computer Vision is the technology that enables machines to see and obtain information from digital images. It is seen as an integral part of AI in fields such as pattern recognition
VI... VIII
- 23 2017 2017 ... VI... VIII... 1 1... 2... 2 1.1... 2 1.1.1... 2 1.1.2... 9 1.1.3... 13 1.1.4... 25 1.1.5... 29 1.1.6... 34 1.1.7... 37 1.1.8... 42 1.2... 47 1.2.1... 47 1.2.2 -... 49 1.2.3... 50 1.2.4
Section 2.3: Simple Linear Regression: Predictions and Inference
Section 2.3: Simple Linear Regression: Predictions and Inference Jared S. Murray The University of Texas at Austin McCombs School of Business Suggested reading: OpenIntro Statistics, Chapter 7.4 1 Simple
Application for registration of a compost, potting soil product, mulch material, substrate, of degradable pots or technical materials
Will be filled automatically: Please fill in this form electronically and send it directly to by using the Send- Button at the end of the form. Sending the forms via letter post is not necessary. Please
Louis 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
CS125 : Introduction to Computer Science. Lecture Notes #11 Procedural Composition and Abstraction. c 2005, 2004 Jason Zych
CS125 : Introduction to Computer Science Lecture Notes #11 Procedural Composition and Abstraction c 2005, 2004 Jason Zych 1 Lecture 11 : Procedural Composition and Abstraction Solving a problem...with
Chapter 5: Summary and Conclusion CHAPTER 5 SUMMARY AND CONCLUSION. Chapter 1: Introduction
CHAPTER 5 SUMMARY AND CONCLUSION Chapter 1: Introduction Data mining is used to extract the hidden, potential, useful and valuable information from very large amount of data. Data mining tools can handle
Deep Learning For Video Classification. Presented by Natalie Carlebach & Gil Sharon
Deep Learning For Video Classification Presented by Natalie Carlebach & Gil Sharon Overview Of Presentation Motivation Challenges of video classification Common datasets 4 different methods presented in
MetaData for Database Mining
MetaData for Database Mining John Cleary, Geoffrey Holmes, Sally Jo Cunningham, and Ian H. Witten Department of Computer Science University of Waikato Hamilton, New Zealand. Abstract: At present, a machine
Gnome Data Mine Tools Evaluation Report
Gnome Data Mine Tools Evaluation Report CMPUT695 Assignment 2 Haobin Li, Junfeng Wu Thursday, November 04, 2004 Overview The gnome-data-mine-tools (GDataMine) is an open source data mining tool set which
Basic Reliable Transport Protocols
Basic Reliable Transport Protocols Do not be alarmed by the length of this guide. There are a lot of pictures. You ve seen in lecture that most of the networks we re dealing with are best-effort : they
Section 8.3 Vector, Parametric, and Symmetric Equations of a Line in
Section 8.3 Vector, Parametric, and Symmetric Equations of a Line in R 3 In Section 8.1, we discussed vector and parametric equations of a line in. In this section, we will continue our discussion, but,
Supervised Learning with Neural Networks. We now look at how an agent might learn to solve a general problem by seeing examples.
Supervised Learning with Neural Networks We now look at how an agent might learn to solve a general problem by seeing examples. Aims: to present an outline of supervised learning as part of AI; to introduce
Predicting Messaging Response Time in a Long Distance Relationship
Predicting Messaging Response Time in a Long Distance Relationship Meng-Chen Shieh m3shieh@ucsd.edu I. Introduction The key to any successful relationship is communication, especially during times when
IPR Äspö Hard Rock Laboratory. International Progress Report. TRUE Block Scale project. Summary of chemical data December 2002
International Progress Report IPR-03-25 Äspö Hard Rock Laboratory TRUE Block Scale project Summary of chemical data December 2002 Jeanette Carmström SKB April 2003 Svensk Kärnbränslehantering AB Swedish
Lab Assignment 1. Part 1: Feature Selection, Cleaning, and Preprocessing to Construct a Data Source as Input
CIS 660 Data Mining Sunnie Chung Lab Assignment 1 The Marketing department of Adventure Works Cycles wants to increase sales by targeting specific customers for a mailing campaign. The company's database
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
FACE RECOGNITION USING SUPPORT VECTOR MACHINES
FACE RECOGNITION USING SUPPORT VECTOR MACHINES Ashwin Swaminathan ashwins@umd.edu ENEE633: Statistical and Neural Pattern Recognition Instructor : Prof. Rama Chellappa Project 2, Part (b) 1. INTRODUCTION
(Refer Slide Time 3:31)
Digital Circuits and Systems Prof. S. Srinivasan Department of Electrical Engineering Indian Institute of Technology Madras Lecture - 5 Logic Simplification In the last lecture we talked about logic functions
CHAPTER-13. Mining Class Comparisons: Discrimination between DifferentClasses: 13.4 Class Description: Presentation of Both Characterization and
CHAPTER-13 Mining Class Comparisons: Discrimination between DifferentClasses: 13.1 Introduction 13.2 Class Comparison Methods and Implementation 13.3 Presentation of Class Comparison Descriptions 13.4
Application for registration of a compost, potting soil product, mulch material, substrate, of degradable pots or technical materials
Will be filled automatically: Please fill in this form electronically and send it directly to by using the Send- Button at the end of the form. Sending the forms via letter post is not necessary. Please
IJSRD - International Journal for Scientific Research & Development Vol. 4, Issue 05, 2016 ISSN (online):
IJSRD - International Journal for Scientific Research & Development Vol. 4, Issue 05, 2016 ISSN (online): 2321-0613 A Study on Handling Missing Values and Noisy Data using WEKA Tool R. Vinodhini 1 A. Rajalakshmi