Imbalanced Learning Based on Data-Partition and SMOTE

摘要: Classification of data with imbalanced class distribution has encountered a significant drawback by most conventional classification learning methods which assume relatively balanced distribution. This paper proposes novel method based on data-partition and SMOTE for learning. The proposed differs from ones in both the prediction stages. For stage, uses following three steps to learn class-imbalance oriented model: (1) partitioning majority into several clusters using partition such as K-Means, (2) constructing training set each obtained merging cluster minority class, (3) model convention including decision tree, SVM neural network. Therefore, classifier repository consisting models is constructed. With respect given example be classified, constructed stage select predict example. Comprehensive experiments KEEL sets show that outperforms some other existing evaluation measures recall, g-mean, f-measure AUC.