Evolutionary Multilabel Feature Selection Using Promising Feature Subset Generation

Abstract

Recent progress in the development of sensor devices improves information harvesting and allows complex but intelligent applications based on learning hidden relations between collected sensor data and objectives. In this scenario, multilabel feature selection can play an important role in achieving better learning accuracy when constrained with limited resources. However, existing multilabel feature selection methods are search-ineffective because generated feature subsets frequently include unimportant features. In addition, only a few feature subsets compared to the search space are considered, yielding feature subsets with low multilabel learning accuracy. In this study, we propose an effective multilabel feature selection method based on a novel feature subset generation procedure. Experimental results demonstrate that the proposed method can identify better feature subsets than conventional methods.