an introduction to weka - nus computing · 2006-04-17 · lecture at national yang ming university,...
TRANSCRIPT
![Page 1: An Introduction to WEKA - NUS Computing · 2006-04-17 · Lecture at National Yang Ming University, June 2006 An Introduction to WEKA Lecture by Limsoon Wong Slides prepared by Dong](https://reader033.vdocument.in/reader033/viewer/2022042222/5ec9007a6b6392450e61258b/html5/thumbnails/1.jpg)
Lecture at National Yang Ming University, June 2006
An Introduction to WEKA
Lecture by Limsoon WongSlides prepared by Dong Difeng
![Page 2: An Introduction to WEKA - NUS Computing · 2006-04-17 · Lecture at National Yang Ming University, June 2006 An Introduction to WEKA Lecture by Limsoon Wong Slides prepared by Dong](https://reader033.vdocument.in/reader033/viewer/2022042222/5ec9007a6b6392450e61258b/html5/thumbnails/2.jpg)
Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
2
Outline
• What is WEKA• Knowledge Flow• Explorer• Why Knowledge Flow• Cross Validation• Reference
![Page 3: An Introduction to WEKA - NUS Computing · 2006-04-17 · Lecture at National Yang Ming University, June 2006 An Introduction to WEKA Lecture by Limsoon Wong Slides prepared by Dong](https://reader033.vdocument.in/reader033/viewer/2022042222/5ec9007a6b6392450e61258b/html5/thumbnails/3.jpg)
Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
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What is WEKA
• Developed at Univ of Waikato in New Zealand
• A collection of state-of-art machine learning algorithms and data preprocessing tools
• Provide implementation of – Regression– Classification– Clustering– Association rules– Feature selection
![Page 4: An Introduction to WEKA - NUS Computing · 2006-04-17 · Lecture at National Yang Ming University, June 2006 An Introduction to WEKA Lecture by Limsoon Wong Slides prepared by Dong](https://reader033.vdocument.in/reader033/viewer/2022042222/5ec9007a6b6392450e61258b/html5/thumbnails/4.jpg)
Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
4
What is WEKA
![Page 5: An Introduction to WEKA - NUS Computing · 2006-04-17 · Lecture at National Yang Ming University, June 2006 An Introduction to WEKA Lecture by Limsoon Wong Slides prepared by Dong](https://reader033.vdocument.in/reader033/viewer/2022042222/5ec9007a6b6392450e61258b/html5/thumbnails/5.jpg)
Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
5
Outline
• What is WEKA• Knowledge Flow• Explorer• Why Knowledge Flow• Cross Validation• Reference
![Page 6: An Introduction to WEKA - NUS Computing · 2006-04-17 · Lecture at National Yang Ming University, June 2006 An Introduction to WEKA Lecture by Limsoon Wong Slides prepared by Dong](https://reader033.vdocument.in/reader033/viewer/2022042222/5ec9007a6b6392450e61258b/html5/thumbnails/6.jpg)
Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
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Knowledge Flow
• Experiment 1:– Type: Classification– Feature selection: GainRatio; Ranker (top 3)– Algorithm: ID3– Training: Weather_nominal.arff– Test: Weather_nominal.arff
![Page 7: An Introduction to WEKA - NUS Computing · 2006-04-17 · Lecture at National Yang Ming University, June 2006 An Introduction to WEKA Lecture by Limsoon Wong Slides prepared by Dong](https://reader033.vdocument.in/reader033/viewer/2022042222/5ec9007a6b6392450e61258b/html5/thumbnails/7.jpg)
Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
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Knowledge Flow
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Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
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Knowledge Flow
• Source file (.ARFF)
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Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
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Knowledge Flow
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Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
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Knowledge Flow
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Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
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Knowledge Flow
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Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
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Knowledge Flow
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Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
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Knowledge Flow
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Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
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Knowledge Flow
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Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
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Knowledge Flow
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Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
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Knowledge Flow
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Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
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Knowledge Flow
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Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
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Knowledge Flow
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Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
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Knowledge Flow
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Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
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Knowledge Flow
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Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
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Knowledge Flow
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Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
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Knowledge Flow
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Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
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Knowledge Flow
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Knowledge Flow
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Knowledge Flow
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Knowledge Flow
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Knowledge Flow
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Knowledge Flow
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Knowledge Flow
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Knowledge Flow
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Knowledge Flow
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Knowledge Flow
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Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
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Knowledge Flow
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Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
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Outline
• What is WEKA• Knowledge Flow• Explorer• Why Knowledge Flow• Cross Validation• Reference
![Page 35: An Introduction to WEKA - NUS Computing · 2006-04-17 · Lecture at National Yang Ming University, June 2006 An Introduction to WEKA Lecture by Limsoon Wong Slides prepared by Dong](https://reader033.vdocument.in/reader033/viewer/2022042222/5ec9007a6b6392450e61258b/html5/thumbnails/35.jpg)
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Explorer
• Do the same experiment…• Experiment 1:
– Type: Classification– Feature selection: GainRatio; Ranker top 3– Algorithm: ID3– Training: Weather_nominal.arff– Test: Weather_nominal.arff
![Page 36: An Introduction to WEKA - NUS Computing · 2006-04-17 · Lecture at National Yang Ming University, June 2006 An Introduction to WEKA Lecture by Limsoon Wong Slides prepared by Dong](https://reader033.vdocument.in/reader033/viewer/2022042222/5ec9007a6b6392450e61258b/html5/thumbnails/36.jpg)
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Explorer
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Explorer
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Explorer
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Explorer
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Explorer
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Explorer
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Outline
• What is WEKA• Knowledge Flow• Explorer• Why Knowledge Flow• Cross Validation• Reference
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Why Knowledge Flow
• There are some jobs we cannot do in explorer …– Combine feature selection– Build more complicated systems
• KF describes the process more clearly– Never regard the training and test data to be
separate in the previous example in explorer• KF help us to access some mid-process info of
the machine learning method– Cross Validation
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Lecture at National Yang Ming University, June 2006 Copyright 2006 © Dong Difeng
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Outline
• What is WEKA• Knowledge Flow• Explorer• Why Knowledge Flow• Cross Validation• Reference
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Cross Validation
• Experiment 2:– Type: Classification– Feature selection: GainRatio; Ranker top 3– Algorithm: ID3– Training: Weather_nominal.arff (CV)– Test: Weather_nominal.arff (CV)– CV type: 3-folder CV
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Cross Validation
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Cross ValidationWhat do we view in this case?Text1 VS. Text2 (1)
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Cross Validation
Text1 VS. Text2 (2)
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Cross Validation
Text1 VS. Text2 (3)
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Cross Validation
Text3 VS. Text4 (1)
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Cross Validation
Text3 VS. Text4 (2)
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Cross Validation
Text3 VS. Text4 (3)
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Cross ValidationTrees
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Cross ValidationEvaluation of result
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Cross Validation
• Conclusion:– Source data are separated into several folders for
cross validation– Feature selection is done for each training folder
(only training) folder separately– Different trees are build in different cases– The evaluation of classification is by overall results
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Cross Validation
• Experiment 3:– Type: Classification– Feature selection: GainRatio; Ranker top 2– Algorithm: ID3– Training: Weather_nominal.arff– Test: Weather_nominal.arff
![Page 57: An Introduction to WEKA - NUS Computing · 2006-04-17 · Lecture at National Yang Ming University, June 2006 An Introduction to WEKA Lecture by Limsoon Wong Slides prepared by Dong](https://reader033.vdocument.in/reader033/viewer/2022042222/5ec9007a6b6392450e61258b/html5/thumbnails/57.jpg)
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Cross Validation
Ranker top 3 VS. Ranker top 2
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Cross Validation
• Conclusion:– Attribute “windy” was ignored. In this case, the
classifier only consider the attribute that was kept
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Reference
• http://www.cs.waikato.ac.nz/~ml/
• Ian H. Witten, Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques (Second Edition)