ohwwlejspbfotbplistscmswaikatoacnz send wekalist mailing list submissions iuxxdkqklistscmswaikatoacnz subscribe unsubscribe via world wide web visit httpslistscmswaikatoacnzmailmanlistinfowekalist via email send message subject body help ohwwlejspbfotbplistscmswaikatoacnz reach person managing list pobypuhshvhzjlistscmswaikatoacnz replying please edit subject line specific contents wekalist digest todays topics 1 questions experimenter peter reutemann 2 question feature selection harri mt saarikoski 3 weka models pmml carmelo saffioti 4 memory independent algorithms carmelo saffioti 5 ignoring attribute rafiqul haque 6 memory independent algorithms peter reutemann 7 ignoring attribute peter reutemann 8 weka models pmml peter reutemann 9 knowledge flow interface clustering variables input clustering algorithm kept reassociated result clustering mark hall dlnirllccswaikatoacnz sat apr 26 155236 2008 message 1 date sat 16 feb 2008 144450 1300 nzdt peter reutemann subject wekalist questions experimenter weka machine learning workbench list messageid 4444540884010391444454088sujmdnjkwebmailscmswaikatoacnz contenttype textplaincharsetiso88591 sure way seperate experiment results one database able achieve everything seems go results0 table automatically use different mysql database experiment want keep seperated experiment setup ie crossvalidationpercentagesplit randomizedfixed parameters defines single experiment basically soon cant compare anymore cant compare crossvalidation percentagesplit results 2fold 10fold cv results directed another table always use setup need create different databases like create different output files 3 one shows summary ranking lot pairwise significance tests used tests bonferroni adjusted corrected shown paired ttest indicate bonferroni correction multiple significance tests different kind correction copypaste wekaexperimentpairedcorrectedttester javadoc behaves pairedttester uses corrected resampled ttest statistic information see claude nadeau yoshua bengio 2001 inference generalization error machine learning dug far little understanding statistics goes corrected ttest corrects underestimate variance one repeated cvs seems automatic bonferroni adjustment least nowhere mentioned weka sorry statistics knowledge even less hth cheers peter peter reutemann dept computer science university waikato nz httpwwwcswaikatoacnzfracpete ph 64 7 8585174 greljzktpquktddhelsinkifi sat apr 26 155236 2008 message 2 date sat 16 feb 2008 114053 0200 harri mt saarikoski subject wekalist question feature selection weka machine learning workbench list messageid 120315485347b6afa57b1c4webmailhelsinkifi contenttype textplain charsetusascii lainaus fang wang hello recently feature selection classification example gene list gene contains 15 features weka using filter get 5 features among initial 15 fetures 5 features using supervised classifer could seperate genes cancerrelated noncancerrelated want use 5 features scan another group genes want try find cancer related brief sentence question 1a list genes gene 15 features already know cancer related 2a list genes investigate gene feature genes idea cancer related genes hi able reduce dimensions five features want classify cancerno extent features predicts instance think sensible classifier naive bayes decision trees instancebased classifier ibk deliver optimal accuracy 95 least found possible mark classes new gene instances test set use crossvalidation 10fold cv explorer recommendedly 10 times 10fold cv experimenter possible unexpected problem come iterative classifiers support vector machines smo libsvm errorminimizing neural network multilayerperceptron possible key parameters set tight even though cv error close zero test error may tend make generalizations carsinogeny value ranges features svm problem comes support vectors instances mark class border defined clearly ie instances highly ambiguous basically could consider using svm several local maxima per feature ie ranges 010 80100 1080 correlated cancer since svm evaluate features part whole instances neural networks problem stop iteration feature weight changes hard estimate lead overfitting ie incorrect feature weights anyway set learning rate parameter high dataset require many iterations low generalization accuracy tend happen stable classifiers naive bayes instancebased classifier lazyibk eg j48 confidence parameter set maximum 050 performs pruning tree basically decision tree trees package therefore would safer choice expect five features work together predict cancer consider bayesnet place naive bayes since assumes features correlated harri suggestion idea question appreciated thanks fang wang _______________________________________________ wekalist mailing list iuxxdkqklistscmswaikatoacnz httpslistscmswaikatoacnzmailmanlistinfowekalist yst terv best wishes harri saarikoski wed 06 aug 2008 110952 0500 wekalist digest vol 60 issue 24