A bidirectional dynamic grouping multi-objective evolutionary algorithm for feature selection on high-dimensional classification

Yu, Kunjie; Sun, Shaoru; Liang, Jing; Chen, Ke; Qu, Boyang; Yue, Caitong; Wang, Ling

doi:10.1016/j.ins.2023.119619

Cited by 6 publications

(2 citation statements)

References 49 publications

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“…When |R| ≤ 1, the SCSO has entered the exploitation phase, and its location update method is shown in Equations (7) and (8).…”

Section: P T+1mentioning

confidence: 99%

“…The problems people encounter in fields such as engineering [1], ecology [2], information [3], manufacturing [4], design [5], and management [6] are becoming increasingly complex. Most of these problems exhibit characteristics such as multi-objective [7] and highdimensional [8]. The swarm intelligence algorithm is a critical way to solve optimization problems [9], with simple principles, easy implementation, and excellent performance; it has been favored by more and more scholars, and research in this area is also increasing [10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Strategy Improved Sand Cat Swarm Optimization: Global Optimization and Feature Selection

Yao,

Yang,

Yuan

et al. 2023

Biomimetics

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The sand cat is a creature suitable for living in the desert. Sand cat swarm optimization (SCSO) is a biomimetic swarm intelligence algorithm, which inspired by the lifestyle of the sand cat. Although the SCSO has achieved good optimization results, it still has drawbacks, such as being prone to falling into local optima, low search efficiency, and limited optimization accuracy due to limitations in some innate biological conditions. To address the corresponding shortcomings, this paper proposes three improved strategies: a novel opposition-based learning strategy, a novel exploration mechanism, and a biological elimination update mechanism. Based on the original SCSO, a multi-strategy improved sand cat swarm optimization (MSCSO) is proposed. To verify the effectiveness of the proposed algorithm, the MSCSO algorithm is applied to two types of problems: global optimization and feature selection. The global optimization includes twenty non-fixed dimensional functions (Dim = 30, 100, and 500) and ten fixed dimensional functions, while feature selection comprises 24 datasets. By analyzing and comparing the mathematical and statistical results from multiple perspectives with several state-of-the-art (SOTA) algorithms, the results show that the proposed MSCSO algorithm has good optimization ability and can adapt to a wide range of optimization problems.

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“…When |R| ≤ 1, the SCSO has entered the exploitation phase, and its location update method is shown in Equations (7) and (8).…”

Section: P T+1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Strategy Improved Sand Cat Swarm Optimization: Global Optimization and Feature Selection

Yao,

Yang,

Yuan

et al. 2023

Biomimetics

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A Correlation-Redundancy Guided Evolutionary Algorithm and Its Application to High-Dimensional Feature Selection in Classification

Sun,

Guo,

Liu

et al. 2024

Neural Process Lett

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The processing of high-dimensional datasets has become unavoidable with the development of information technology. Most of the literature on feature selection (FS) of high-dimensional datasets focuses on improvements in search strategies, ignoring the characteristics of the dataset itself such as the correlation and redundancy of each feature. This could degrade the algorithm's search effectiveness. Thus, this paper proposes a correlation-redundancy guided evolutionary algorithm (CRGEA) to address high-dimensional FS with the objectives of optimizing classification accuracy and the number of features simultaneously. A new correlation-redundancy assessment method is designed for selecting features with high relevance and low redundancy to speed up the entire evolutionary process. In CRGEA, a novel initialization strategy combined with a multiple threshold selection mechanism is developed to produce a high-quality initial population. A local acceleration evolution strategy based on a parallel simulated annealing algorithm and a pruning method is developed, which can search in different directions and perform deep searches combing the annealing stage around the best solutions to improve the local search ability. Finally, the comparison experiments on 16 public high-dimensional datasets verify that the designed CRGEA outperforms other state-of-the-art intelligent algorithms. The CRGEA can efficiently reduce redundant features while ensuring high accuracy.

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An adaptive dual-strategy constrained optimization-based coevolutionary optimizer for high-dimensional feature selection

Li,

Zhang,

Yang

et al. 2024

Computers and Electrical Engineering

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A bidirectional dynamic grouping multi-objective evolutionary algorithm for feature selection on high-dimensional classification

Cited by 6 publications

References 49 publications

Multi-Strategy Improved Sand Cat Swarm Optimization: Global Optimization and Feature Selection

Multi-Strategy Improved Sand Cat Swarm Optimization: Global Optimization and Feature Selection

A Correlation-Redundancy Guided Evolutionary Algorithm and Its Application to High-Dimensional Feature Selection in Classification

An adaptive dual-strategy constrained optimization-based coevolutionary optimizer for high-dimensional feature selection

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