Feature Selection Using Fuzzy Neighborhood Entropy-Based Uncertainty Measures for Fuzzy Neighborhood Multigranulation Rough Sets

For heterogeneous data sets containing numerical and symbolic feature values, feature selection based on fuzzy neighborhood multigranulation rough sets (FNMRS) is a very significant step to preprocess data and improve its classification performance. This article presents an FNMRS-based feature selec...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems 2021-01, Vol.29 (1), p.19-33
Main Authors: Sun, Lin, Wang, Lanying, Ding, Weiping, Qian, Yuhua, Xu, Jiucheng
Format: Article
Language:English
Subjects:
Algorithms
Classification
Datasets
Entropy
Feature extraction
Feature selection
fuzzy neighborhood rough sets (FNRS)
Fuzzy sets
Measurement uncertainty
neighborhood entropy
neighborhood multigranulation rough sets (NMRS)
Numerical models
Rough sets
Uncertainty
uncertainty measure
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Summary:For heterogeneous data sets containing numerical and symbolic feature values, feature selection based on fuzzy neighborhood multigranulation rough sets (FNMRS) is a very significant step to preprocess data and improve its classification performance. This article presents an FNMRS-based feature selection approach in neighborhood decision systems. First, some concepts of fuzzy neighborhood rough sets and neighborhood multigranulation rough sets are given, and then the FNMRS model is investigated to construct uncertainty measures. Second, the optimistic and pessimistic FNMRS models are built by using fuzzy neighborhood multigranulation lower and upper approximations from algebra view, and some fuzzy neighborhood entropy-based uncertainty measures are developed in information view. Inspired by both algebra and information views based on the FNMRS model, the fuzzy neighborhood pessimistic multigranulation entropy is proposed. Third, the Fisher score model is utilized to delete irrelevant features to decrease the complexity of high-dimensional data sets, and then, a forward feature selection algorithm is provided to promote the performance of heterogeneous data classification. Experimental results on 12 data sets show that the presented model is effective for selecting important features with the higher stability of classification in neighborhood decision systems.
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2020.2989098