Feature selection aims at finding the minimum number of features that result in high classification accuracy. Accordingly, the feature selection is considered as a multi-objective problem. However, most existing approaches treat feature selection as single-objective problem, and they are often divided into two main categories: filter and wrapper methods. Filters are known as fast methods but less accurate, while wrappers are computationally expensive but with high classification performance. This paper proposes a novel hybrid filter-wrapper feature selection approach using whale optimization algorithm (WOA). The proposed method is a multi-objective algorithm in which a filter and wrapper fitness functions are optimized simultaneously. Our algorithm’s efficiency is demonstrated through an extensive comparison with seven well-known algorithms on twelve benchmark datasets. Experimental results show the ability of the proposed algorithm to obtain several subsets that include smaller number of features with excellent classification accuracy.

特征选择旨在找到导致高分类准确率的最少特征数。因此，特征选择被认为是一个多目标问题。然而，大多数现有方法将特征选择视为单目标问题，它们通常分为两大类：过滤器和包装器方法。过滤器被称为快速方法但不太准确，而包装器计算成本高但分类性能高。本文提出了一种使用鲸鱼优化算法 (WOA) 的新型混合滤波器包装器特征选择方法。所提出的方法是一种多目标算法，其中同时优化过滤器和包装器适应度函数。通过在十二个基准数据集上与七种著名算法的广泛比较，证明了我们算法的效率。实验结果表明，所提出的算法能够以优异的分类精度获得包含较少特征的几个子集。