进化算法
特征选择
编码(内存)
进化计算
接头(建筑物)
计算机科学
人工智能
选择(遗传算法)
多目标优化
算法
模式识别(心理学)
特征(语言学)
遗传算法
统计分类
机器学习
工程类
哲学
语言学
建筑工程
作者
Jing Liang,Junting Yang,Caitong Yue,Ying Bi,Kunjie Yu,Boyang Qu,Yuyang Zhang,Mengmeng Li
标识
DOI:10.1109/tevc.2025.3529977
摘要
In multiobjective feature selection, different feature subsets with the same number of selected features can achieve identical classification accuracy, meaning that it is a multimodal optimization problem. To effectively search for multimodal feature subsets within the vast search spaces of high-dimensional datasets, it is crucial to adopt reasonable encoding and search methods. Generally, applying a uniform evolutionary operator based on a single encoding method across the entire feature space is inefficient and prone to falling into local optima. To address the above issues, this article proposes a multimodal multiobjective feature selection method based on a joint encoding mechanism that combines discrete encoding and continuous encoding. It provides new perspectives to solve the high-dimensional feature selection problem from encoding methods to search operators. First, the search space is divided into a discrete encoding region and a continuous encoding region based on the knee points of feature importance ranking curve. A tailored initialization strategy is used to obtain the initial population for joint encoding. Second, an adaptive niche strategy based on three priorities is proposed, which ensures the similarity of individuals within a niche and the difference between niches. In addition, different search operators are cooperated with the two encoding strategies, respectively, to achieve effective and efficient search. The experimental results on 24 datasets show that the proposed algorithm achieves a better-classification performance than the state-of-the-art feature selection methods.
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