1、Advanced Topics on ClassificationQuan Zou (鄒 權(quán)) (Ph.D.& Assistant Professor)9/22/20221OutlineImbalance Binary ClassificationMulti Class, Multi Label ClassificationMulti Instance ClassificationSemi-supervised and Transductive ClassificationEnsemble LearningOthers9/22/20222Imbalance binary classificat

2、ionApplication：Credit Card CheatSpam IdentificationFinding OilBioinformatics9/22/20223Imbalance binary classificationStrategy of samplingOver-samplingUnder-samplingRandom-samplingSpecial-sampling (SMOTE)Strategy of costEqual to aboveOne-class leaning9/22/20224mulan9/22/202269/22/20227Multi Instance

3、ClassificationDrug Design, Image UnderstandingPackage, Instance DD9/22/202299/22/202210Semi-supervised and Transductive ClassificationSemi-supervised ClassificationUnlabeled samples are importantCo-training and Tri-training9/22/202211Transductive Classification9/22/2022129/22/202213Ensemble learning

4、Bagging9/22/202214Ensemble learningRandom Forest9/22/202216Ensemble learning for Class Imbalance Problem9/22/202217StrategyFirst, the negative set is divided randomly into several subsets equally. Every subset together with the positive set is a class balance training set. Then several different cla

5、ssifiers are selected and trained with these balance training sets. They will vote for the last prediction when facing new samples.The samples 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 9/22/202219OthersActive learningLazy learningParallel learning (mahout)OptimizationFeatures Sele