qydlqcwsiacfymissuesapacheorg httpsissuesapacheorgspamassassinshow_bugcgiid5717 additional comments user7gvcceaschallengecc 20080225 0844 reply comment 8 bug 5041 situation even likely current default sa size limit 500 kb according spamc man page default chunk size outlook chops messagepartial chunks ok suggest least kill easilyfixed part scanning messagepartial sure rest though receiving mail assignee bug watching assignee greljzktpquktddhelsinkifi sat apr 26 154855 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 charset3dusascii 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 thu 07 aug 2008 162455 0800 bug 5717 revert rawbody rule type perline regexps avoid dos problems