Feature Selection Combining Information Theory View and Algebraic View in the Neighborhood Decision System

Feature selection is one of the core contents of rough set theory and application. Since the reduction ability and classification performance of many feature selection algorithms based on rough set theory and its extensions are not ideal, this paper proposes a feature selection algorithm that combin...

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Vydáno v:Entropy (Basel, Switzerland) Ročník 23; číslo 6; s. 704
Hlavní autoři: Xu, Jiucheng, Qu, Kanglin, Yuan, Meng, Yang, Jie
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 02.06.2021
MDPI
Témata:
Algebra
algebraic view
Algorithms
Approximation
Classification
Credibility
Datasets
Decision making
Decision theory
Entropy
Entropy (Information theory)
Feature selection
Heuristic
Information theory
information theory view
neighborhood rough set
Neighborhoods
non-monotonicity
Rough set models
Set theory
Statistical analysis
ISSN:1099-4300, 1099-4300
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Shrnutí:Feature selection is one of the core contents of rough set theory and application. Since the reduction ability and classification performance of many feature selection algorithms based on rough set theory and its extensions are not ideal, this paper proposes a feature selection algorithm that combines the information theory view and algebraic view in the neighborhood decision system. First, the neighborhood relationship in the neighborhood rough set model is used to retain the classification information of continuous data, to study some uncertainty measures of neighborhood information entropy. Second, to fully reflect the decision ability and classification performance of the neighborhood system, the neighborhood credibility and neighborhood coverage are defined and introduced into the neighborhood joint entropy. Third, a feature selection algorithm based on neighborhood joint entropy is designed, which improves the disadvantage that most feature selection algorithms only consider information theory definition or algebraic definition. Finally, experiments and statistical analyses on nine data sets prove that the algorithm can effectively select the optimal feature subset, and the selection result can maintain or improve the classification performance of the data set.
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ISSN:1099-4300
1099-4300
DOI:10.3390/e23060704