A multiple surrogate-assisted hybrid evolutionary feature selection algorithm

IF 8.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Wan-qiu Zhang , Ying Hu , Yong Zhang , Zi-wang Zheng , Chao Peng , Xianfang Song , Dunwei Gong
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引用次数: 0

Abstract

Feature selection (FS) is an important data processing technology. However, existing FS methods based on evolutionary computation have still the problems of “curse of dimensionality” and high computational cost, with the increase of the number of feature and/or the size of instance. In view of this, the paper proposes a multiple surrogate-assisted hybrid evolutionary feature selection (MSa-HEFS). Two kinds of surrogates (i.e., objective regression surrogate and sample surrogate) and two kinds of FS methods (i.e., filter and wrapper) are integrated into MSa-HEFS to improve its performance. Firstly, an ensemble filter FS method is designed to reduce the search space of subsequent wrapper evolutionary FS method. Secondly, in the proposed evolutionary FS method, a dual-surrogate-assisted hierarchical individual evaluation mechanism is developed to reduce the evaluation cost on feature subsets, an online management and update strategy is used to adaptively choose appropriate surrogates for individuals. The proposed algorithm is applied to 12 typical datasets and compared with 4 state-of-the-art FS algorithms. Experimental results show that MSa-HEFS can obtain good feature subsets at the smallest computational cost on all datasets. MSa-HEFS source code is available on Github at https://github.com/ZZW-zq/MSa-HEFS-/tree/master.
特征选择(FS)是一项重要的数据处理技术。然而,随着特征数量和/或实例规模的增加,现有的基于进化计算的特征选择方法仍然存在 "维度诅咒 "和计算成本高等问题。有鉴于此,本文提出了一种多代理辅助混合进化特征选择(MSa-HEFS)。在 MSa-HEFS 中集成了两种代理(即客观回归代理和样本代理)和两种 FS 方法(即过滤器和包装器),以提高其性能。首先,设计了一种集合滤波 FS 方法,以减少后续包装进化 FS 方法的搜索空间。其次,在所提出的进化 FS 方法中,开发了双代用辅助分层个体评价机制,以降低特征子集的评价成本,并使用在线管理和更新策略自适应地为个体选择合适的代用。将所提出的算法应用于 12 个典型数据集,并与 4 种最先进的 FS 算法进行了比较。实验结果表明,MSa-HEFS 在所有数据集上都能以最小的计算成本获得良好的特征子集。MSa-HEFS 的源代码可在 Github 上获取:https://github.com/ZZW-zq/MSa-HEFS-/tree/master。
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来源期刊
Swarm and Evolutionary Computation
Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
16.00
自引率
12.00%
发文量
169
期刊介绍: Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.
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