Methods for adapting the leaping frog algorithm to the binary search space when solving the feature selection problem

M. Bardamova, Radioelectronics, A. Buymov, V. Tarasenko
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Abstract

The feature selection is an important step in constructing any classifier. Binary versions of metaheuristic optimization algorithms are often used for selection. However, many metaheuristics are originally created to work in the continuous search space, so they need to be specially adapted to the binary space. In this paper, the authors propose fifteen ways to binarize the Shuffled frog leaping algorithm based on the following methods: modified algebraic operations, merge operation, and transformation functions. The efficiency of the binary algorithm was tested in the problem of feature selection for fuzzy classifiers on data sets from the KEEL repository. The results show that all the described methods of binarization allow reducing the features, while increasing the overall accuracy of classification.
在解决特征选择问题时,将跳蛙算法应用于二叉搜索空间的方法
特征选择是构造分类器的重要步骤。二元版本的元启发式优化算法通常用于选择。然而,许多元启发式最初是为了在连续搜索空间中工作而创建的,因此它们需要特别适应二进制空间。本文基于修正代数运算、归并运算和变换函数等方法,提出了对shuffledfrog跳跃算法进行二值化的15种方法。在KEEL库数据集的模糊分类器特征选择问题上,验证了二值化算法的有效性。结果表明,所有描述的二值化方法都可以减少特征,同时提高分类的整体精度。
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