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首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >Chaotic Atom Search Optimization for Feature Selection
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Chaotic Atom Search Optimization for Feature Selection

机译：Chaotic Atom搜索特征选择的优化

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摘要

Due to the lack of experience and prior knowledge, the selection of the most informative features has become one of the challenging problems in many applications. Recently, many metaheuristic algorithms have widely used to solve the feature selection problem for classification tasks. In this paper, the chaotic atom search optimization (CASO) that integrates the chaotic maps into atom search optimization (ASO) is applied for wrapper feature selection. Twelve different chaotic maps are used to adjust the parameter of CASO through the optimization process, which is beneficial for enhancing the convergence rate and improving the efficiency of ASO algorithm. In this study, twenty benchmark datasets acquired from the UCI machine learning repository are used to validate the performance of CASO in feature selection. Several state-of-the-art metaheuristic algorithms are adopted to examine the efficacy and effectiveness of the proposed approach. Our results indicated that the Logistic-Tent map was the most suitable chaotic map to boost the performance of CASO. The experimental result shows the capability of CASO not only in finding the optimal solution but also in significantly improving the prediction accuracy and reducing the number of features.
机译：由于缺乏经验和先验知识，选择最具信息丰富的功能已成为许多应用中的挑战性问题之一。最近，许多成群质算法已经广泛用于解决分类任务的特征选择问题。在本文中，将混沌映射集成到Atom搜索优化（ASO）中集成了混沌映射的混沌原子搜索优化（CASO）用于包装器特征选择。 12个不同的混沌映射用于通过优化过程调整CASO的参数，这有利于提高收敛速率并提高ASO算法的效率。在本研究中，从UCI机器学习存储库获取的二十个基准数据集用于验证CASO在特征选择中的性能。采用了几种最先进的综合算法来检查所提出的方法的功效和有效性。我们的结果表明，Logistic-Tent Map是最适合促进CASO性能的混沌图。实验结果表明CASO的能力不仅在找到最佳解决方案时，而且还可以显着提高预测精度和减少特征的数量。

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