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© 2013, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 2
1. Usage of SVM and Decision Tree in Weka2. Amplification about Final Project Spec3. SVM – State of the Art in Classification
4. Commentary for Results on Mid-Term Project
5. Useful Technique for Final Project6. Decision Tree
Contents
© 2013, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 4
Neural Networks
• MLP (Multilayer Perceptron)
– In Weka, Classifiers-functions-MultilayerPerceptron
© 2013, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 5
Support Vector Machines
SMO (sequential minimal optimization) for training SVM In Weka, classifiers-functions-SMO
© 2013, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 6
Decision Trees
• J48 (Java implementation of C4.5)
– In Weka, classifiers-trees-J48
© 2013, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 7
Some Notes on the Parameter Setting
• Parameter Setting = Car Tuning– need much experience or many times of trial – you may get worse results if you are unlucky
• Multilayer Perceptron (MLP)– Main parameters for learning: hiddenLayers, learningRate, momentum,
trainingTime (epoch), seed
• SMO (SVM)– Main parameters: c (complexity parameter), kernel, kernel parameters
• J48– Main parameters: unpruned, numFolds, minNumObj– Many parameters are for controlling the size of the result tree, i.e. confi-
denceFactor, pruning
Final Project (Mandatory)• Extension experiments of midterm project• Methods (2 mandatory algorithm)– Must Use Weka
• MLP (used in midterm)• SVM or Decision Tree (mandatory)
• If you just use 2 mandatory algorithm (MLP and one more) and make rea-sonable result, you get default point.
Final Project (Optional)
• Methods (1 more optional algorithm)– Must Use Weka
• One arbitrary algorithm in weka
• Optional issue– Proper arbitrary algorithm for data– Proper preprocessing for data– Dataset used in midterm project
• If you are interested in this project, you could try optional issue and you get bonus point.
Final Report
• Final report and presentation– Submit report– About: Design your problem– Include
• Introduction• Problem definition• Dataset• Preprocess (optional)• Methods• Experimental results and comparison• Discussion
• Due date: 2013. 06. 10. PM 23:59• Submit by email ([email protected])
© 2013, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 14
Must Preprocess be done with weka? No. You can use anything for preprocess But, there is some preprocess tool in weka.
Isn’t it preferred to use Extension of Mid-term Dataset? It is more preferred to use Extension of Mid-
term Dataset.
If you do more whatever interesting for project, you will get more point!
But, it is ok just to satisfy mandatory one.
Q&A