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A Clustering Resampling Stacked Ensemble Method for Imbalance Classification Problem by Jian Li; Jinlian Du; Xiao Zhang is a scholarly article available to read on EtoBox.

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The results of the existing research on ensemble methods based on resampling are the best for the imbalance classification task, whereas the results of independentuse of resampling or ensemble learning are relatively mediocre. Compared with the othertwo ensemblestrategiessuch as boosting and bagging,stacking is often better than iterative boostingin training speed becauseitisan ensemblestrategy that parallels baseclassifiers. Bagginguses a weighted averageat the decision level, whereasstacking uses a machinelearningmodel to make decisions, which achieves higher accuracy than baggingusing a weightedaveragedecision. The effect of stackingrelies heavily on the varianceof the baseclassifiers, anditis often better to ensure that the base classifiers have good learningperformancewhile making the variance amongbase classifiers as large as possible. The keyto imbalancedclassification isto deal with the class imbalanceand overlappingin trainingsets.Inthis study, wewill usea hierarchicalapproach tosolve thesetwo problems.Thus, we proposea resampling-based stackingensemblemethodcombined with clustering. The proposed method uses a support vector machine as the base classifier tosolve the probl

Author

Jian Li; Jinlian Du; Xiao Zhang

Publisher

IEEE

Published

2022

Language

EN