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A fuzzy-autoencoder-based evolutionary multiobjective algorithm for high-dimensional feature selection
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DOI：10.1016/j.neucom.2026.134786.png)
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Abstract
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High-dimensional datasets often contain numerous irrelevant or redundant features, which may lead to overfit ting and increased computational complexity. In most cases, feature selection can improve classification accuracy and reduce feature dimensionality, and thus can be regarded as a multi-objective optimization problem. In recent years, multi-objective evolutionary algorithms (MOEAs) have been demonstrated to perform well in multi-objective feature selection tasks. However, their performance often degrades when confronted with high-dimensional noisy data. To address these challenges, this paper proposes an evolutionary algorithm utilizing fuzzy autoencoders, termed FAE-MOEA, for multi-objective feature selection. The proposed method combines fuzzy theory with an autoencoder model to effectively suppress noise in the data and further enhance the dis criminative capability of feature importance. Specifically, we employ mutual information in conjunction with k-means to classify feature correlations, then utilize intuitionistic fuzzy sets to handle uncertainty, and finally extract feature importance through an autoencoder equipped with an Evaluation layer. Moreover, to accelerate the optimization process and extract superior feature subsets, FAE-MOEA initializes a Guidance Matrix based on Feature Importance and Individual Importance, and uses this matrix to guide the evolution of the population. Accordingly, two key operators and an update strategy are designed to effectively direct the evolutionary search process during each iteration. Experimental results on twelve high-dimensional datasets show that the proposed algorithm achieves significant improvements over several existing algorithms with respect to the dimensionality of the retained subsets and the resulting classification performance.
Keywords:
Evolutionary algorithm
Feature selection
Autoencoder
Fuzzy set
Multiobjective optimization
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