Abstract With the advance of generating high-dimensional data, feature selection is the most significant procedure to guarantee selecting the most discriminative subset of features and to improve the classification performance. As a result, a binary black hole optimization algorithm (CBBHA) has been developed by getting inspired from natural phenomena. In this paper, the most discriminating features are selected by a new chaotic binary black hole algorithm (CBBHA) where chaotic maps embedded with movement of stars in the BBHA. Ten chaotic maps are employed. Experiments on three chemical datasets show the proposed algorithm, CBBHA, has an advantage over the standard BBHA in terms of selecting relevant features with a high classification performance. Additionally the performance of CBBHA is compared with BBHA in term of the computational time efficiency which is revealing that CBBHA outperforms the BBHA.

中文翻译：

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基于混沌二值黑洞算法的数据分类特征选择

摘要 随着高维数据生成的进步，特征选择是保证选择最具判别力的特征子集和提高分类性能的最重要过程。因此，受自然现象的启发，开发了一种二进制黑洞优化算法 (CBBHA)。在本文中，最具辨别力的特征是通过一种新的混沌二元黑洞算法（CBBHA）选择的，其中混沌图嵌入了 BBHA 中恒星的运动。使用了十张混沌图。在三个化学数据集上的实验表明，所提出的算法 CBBHA 在选择具有高分类性能的相关特征方面优于标准 BBHA。