A binary dandelion algorithm using seeding and chaos population strategies for feature selection

Feature selection (FS) is an important pre-processing step in data mining and pattern recognition. It can effectively compress the dimensionality of the feature space to reduce computation time and improve classification performance. The meta-heuristic algorithm-based feature selection method by fin...

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Vydané v:Applied soft computing Ročník 125; s. 109166
Hlavní autori: Zhao, Yuxin, Dong, Junwei, Li, Xiaobo, Chen, Hui, Li, Shaolang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.08.2022
Predmet:
Chaos algorithm
Dandelion algorithm
Feature selection
Metaheuristic algorithm
Seeding strategy
Chaos algorithm
Feature selection
Seeding strategy
Metaheuristic algorithm
Dandelion algorithm
ISSN:1568-4946, 1872-9681
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Popis
Shrnutí:Feature selection (FS) is an important pre-processing step in data mining and pattern recognition. It can effectively compress the dimensionality of the feature space to reduce computation time and improve classification performance. The meta-heuristic algorithm-based feature selection method by finding the optimal set of features in the solution space has been widely used. However, this method is prone to trap into local optimality in a sufficiently large solution space. In this paper, we first propose a binary dandelion algorithm (BDA) to improve classification accuracy. In addition, to improve the performance of the algorithm, a binary dandelion algorithm using an improved seeding strategy and chaotic populations (SBDA) is proposed in this paper. Firstly, the strategy of optimizing the seeding radius by using the vibrational function and the historical optimal population increases the complexity of the search process and improves the search performance of the algorithm in the solution space. Secondly, when generating seeds, chaotic populations are generated using chaotic operators, which improves the ability of the algorithm to jump out of the local optimum and improves the stability of the algorithm. In this paper, 15 well-established datasets collected from the UCI machine learning database were adopted to compare four variants of BDA using only chaotic population improvement and in the next experiments, both mechanisms are verified to be effective in improving the performance of the algorithm. In addition, this paper compares the proposed BDA algorithm and SBDA algorithm with eight other classical algorithms. The experimental results show that SBDA can obtain fewer features with higher classification accuracy in most cases. •Proposed a BDA algorithm for solving binary problems.•Proposed a SBDA algorithm by incorporating chaos mapping.•Presented an improved seeding strategy in relation to the historical optimal population.•Studied and applied the feature selection problem with extreme learning machine.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2022.109166