分類高級課題AdvancedTopicsonClassification教學(xué)課件

1、advanced topics on classificationquan zou (鄒 權(quán)) (ph.d.& assistant professor)2021-10-23http:/2/20outlineimbalance binary classificationmulti class, multi label classificationmulti instance classificationsemi-supervised and transductive classificationensemble learningothers2021-10-23http:/3/20imbalanc

2、e binary classificationapplication：credit card cheatspam identificationfinding oilbioinformatics2021-10-23http:/4/20imbalance binary classification strategy of sampling over-sampling under-sampling random-sampling special-sampling (smote) strategy of cost equal to above one-class leaning2021-10-23ht

3、tp:/5/20multi class, multi label multi class one vs one (time consuming) one vs all (imbalance) tree multi label jrs (/challenge/jrs12contest) text, image classification knn meka, mulan2021-10-23http:/6/20mulan2021-10-23http:/7/202021-10-23http:/8/20meka2021-10-23http:/9/20multi ins

4、tance classificationdrug design, image understandingpackage, instance dd2021-10-23http:/10/202021-10-23http:/11/20semi-supervised and transductive classificationsemi-supervised classificationunlabeled samples are importantco-training and tri-training2021-10-23http:/12/20transductive classification20

5、21-10-23http:/13/202021-10-23http:/14/20ensemble learningbagging2021-10-23http:/15/20ensemble learningboosting2021-10-23http:/16/20ensemble learningrandom forest2021-10-23http:/17/20ensemble learning for class imbalance problem2021-10-23http:/18/202021-10-23http:/19/20 strategy first, the negative s

6、et is divided randomly into several subsets equally. every subset together with the positive set is a class balance training set. then several different classifiers are selected and trained with these balance training sets. they will vote for the last prediction when facing new samples. the samples

7、will be added to the next two classifiers training sets if they are misclassified. reference 鄒權(quán), 郭茂祖, 劉揚, 王峻. 類別不平衡的分類方法及在生物信息學(xué)中的應(yīng)用. 計算機研究與發(fā)展. 2010,47(8):1407-1414 x.-y. liu, j. wu, and z.-h. zhou. exploratory undersampling for class-imbalance learning. ieee transactions on systems, man, and cybernetics - part b: cybernetics, 2009, 39(2): 539-550 2021-10-23http:/20/20othersactive learninglazy learning