A Possibilistic Information Fusion-Based Unsupervised Feature Selection Method Using Information Quality Measures

Abstract

The main goal of most information quality (IQ)-based measures is to combine data provided by multiple information sources to enhance the quality of information essential for decision makers to perform their tasks. However, there is few work to fuse multi-source information from the perspective of possibility distribution (PD) and use IQ as the evaluation criteria for feature selection. The PD is one of important concepts in possibility theory, which is a generally acknowledged method for describing a kind of uncertain knowledge. In this paper, we propose a novel representation model of possibility distributions based on fuzzy multisets, namely, a possibility distribution information system (PDIS). Then, several IQ measures are defined in the PDIS, including Gini entropy, compatibility, conflict, credibility and separability degrees. In view of this, a minimal-separability-minimal-uncertainty-based unsupervised feature selection algorithm (UmSMU) is designed. The proposed UmSMU can sufficiently fuse multiple possibilistic information. Meanwhile, the selected features maintain as much information as possible while minimizing the uncertainty of information. The experimental results show that the proposed algorithm performs well, especially when it comes to selecting fewer features and improving performance.