Partial random under/oversampling for multilabel problems

Abstract

Many current data mining applications address problems with instances that belong to more than one class. The term multilabel classification has been introduced as a way of describing this task. Advantageously using the correlation among the labels can provide better performance than methods that manage each label separately. One of the major challenges in multilabel datasets is the class-imbalance problem. In most cases, several or many of the labels are sparsely populated, producing heavily imbalanced datasets. Standard methods used for single-label class-imbalanced datasets are not easily applicable due to the lack of a proper concept of minority instance in the multilabel case. In this paper, we propose a new approach based on partial undersampling and/or oversampling of the instances that is more suitable for the multilabel case. The method modifies the concept of undersampling and oversampling to implement partial undersampling/oversampling of instances. In a large set of 55 real-world multilabel problems, our approach improves the results of current methods for dealing with class-imbalanced datasets in multilabel problems.