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

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.