1. Interactive and Complementary Feature Selection via Fuzzy Multigranularity Uncertainty Measures
- Author
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Jia Liu, Zhong Yuan, Tianrui Li, Jihong Wan, Hongmei Chen, and Wei Huang
- Subjects
Uncertain data ,Computer science ,business.industry ,Feature vector ,Feature selection ,Machine learning ,computer.software_genre ,Fuzzy logic ,Computer Science Applications ,Human-Computer Interaction ,Interactivity ,Control and Systems Engineering ,Feature (computer vision) ,Complementarity (molecular biology) ,Rough set ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,computer ,Software ,Information Systems - Abstract
Feature selection has been studied by many researchers using information theory to select the most informative features. Up to now, however, little attention has been paid to the interactivity and complementarity between features and their relationships. In addition, most of the approaches do not cope well with fuzzy and uncertain data and are not adaptable to the distribution characteristics of data. Therefore, to make up for these two deficiencies, a novel interactive and complementary feature selection approach based on fuzzy multineighborhood rough set model (ICFS_FmNRS) is proposed. First, fuzzy multineighborhood granules are constructed to better adapt to the data distribution. Second, feature multicorrelations (i.e., relevancy, redundancy, interactivity, and complementarity) are considered and defined comprehensively using fuzzy multigranularity uncertainty measures. Next, the features with interactivity and complementarity are mined by the forward iterative selection strategy. Finally, compared with the benchmark approaches on several datasets, the experimental results show that ICFS_FmNRS effectively improves the classification performance of feature subsets while reducing the dimension of feature space.
- Published
- 2023