Journal
IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume 31, Issue 9, Pages 2975-2988Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2023.3238803
Keywords
Feature extraction; Uncertainty; Information systems; Fuses; Entropy; Fuzzy sets; Redundancy; Feature selection; fuzzy multisets (FMs); information quality (IQ) measure; possibilistic information fusion; possibility distribution (PD)
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This article proposes a novel information system based on possibility distribution, along with several defined measures of information quality. Based on this, an unsupervised feature selection algorithm is designed, which can effectively combine multiple possibilistic information while minimizing information uncertainty.
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 multisource 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 the possibility theory, which is a generally acknowledged method for describing a kind of uncertain knowledge. In this article, we propose a novel representation model of PDs based on FMs, 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.
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