Feature selection for label distribution learning using dual-similarity based neighborhood fuzzy entropy

Abstract

Label distribution learning (LDL) is a novel framework for handling label ambiguity problems and has been used widely in practice. However, dealing with high-dimensional data or data with redundant features in the LDL context is still an open problem. Existing feature selection algorithms cannot be directly applied to LDL due to the unique challenges caused by the label uncertainty nature. In this paper, we propose a novel LDL feature selection algorithm based on neighborhood rough sets. Specifically, we first introduce dual-similarity that is used to measure sample similarity in both the feature and the label spaces. Second, we invent a novel neighborhood fuzzy entropy as a feature evaluation metric, with which neighborhood rough sets can be applied to deal with LDL problems. Lastly, we complete a feature selection model that inherits the spirit of neighborhood rough sets and neighborhood fuzzy entropy. Extensive experiments have been conducted on twelve real-world LDL datasets, and the results demonstrate the superiority of our proposed model against to other six state-of-the-art algorithms.