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Set-based integer-coded fuzzy granular evolutionary algorithms for high-dimensional feature selection
DOI:10.1016/j.asoc.2023.110240.png)
摘要
En 中文
Feature selection plays a pivotal role in handling today's high-dimensional databases by keeping only the most valuable features, leading to less computation, improved performance, and higher transparency in decision-making processes. Despite the considerable advances in combinatorial optimization, this data-preprocessing step is computationally NP-hard and continues to pose critical challenges, particularly for very high-dimensional (VHD) databases. Here, we propose integer coding and fuzzy granulation (FG) as an integral part of evolutionary wrapper-based feature selection. Based on this integer coding, we further propose crossover and mutation operators that employ set operations such as 'union,' 'intersection,' and 'complement' for higher transparency in their evolutionary explorative and exploitative search processes. In addition to its common use as a surrogate technique to avoid unnecessary computations by recognizing similarities, the fuzzy granulation concept also operates as a repulsive strategy that searches for dissimilarities in the elitist and population initialization routines to reach higher population diversity. An ablation study is implemented to discover the role of individual components of this multi-prong approach. The results are then compared on 22 benchmark problems, ranging from 64 to 138672 attributes, with nine competing methods. Superior performance is shown for the proposed approach in terms of accuracy (in 15 of 22 cases) and achieving a substantially smaller (as much as six times less) feature set with considerably less computational cost (by an average of 30 percent), particularly for VHD feature selection & COPY; 2023 Elsevier B.V. All rights reserved.
Keyword:
High dimensionality
Wrapper feature selection
Integer representation
Set operations
Evolutionary algorithms
Fuzzy granulation
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
Self-adaptive parameter and strategy based particle swarm optimization for large-scale feature selection problems with multiple classifiers基于自适应参数和策略的粒子群算法求解大规模多分类器特征选择问题
Feature selection using bare-bones particle swarm optimization with mutual information基于互信息的裸机粒子群优化算法的特征选择
PATTERN RECOGNITION
IF7.6
A novel hybrid genetic algorithm with granular information for feature selection and optimization一种基于粒度信息的特征选择与优化混合遗传算法

