Online multi-label feature selection on imbalanced data sets

Abstract

Feature selection is an important step of data processing. When feature selection is conducted for multi-label classification problem in online learning fashion, it is the problem of online multi-label feature selection. Online feature selection is very appropriate for some actual situations in which the data is not available in advance, the data size is very large or fast running speed is highly demanding. We propose an online multi-label feature selection algorithm in which the data set is divided into many single-label data sets, feature selection is conducted for each single-label data set and the final features are selected from the selected single-label features. As many data sets are imbalanced, we use the basic idea of cost-sensitive learning to combat it. Experiment results corroborate the performance of our algorithm on various data sets and demonstrate that the proposed algorithm can improve online classification performance on imbalanced data sets effectively.