An Efficient Moth Flame Optimization Algorithm using Chaotic Maps for Feature Selection in the Medical Applications

In this paper, multiple variants of the Binary Moth Flame Optimization Algorithm (BMFO) based on chaotic maps are introduced and compared as search strategies in a wrapper feature selection framework. The main purpose of using chaotic maps is to enhance the initialization process of solutions in order to help the optimizer alleviate the local minima and globally converge towards the optimal solution. The proposed approaches are applied for the first time on FS problems. Dimensionality is a major problem that adversely impacts the learning process due to data-overfit and long learning time. Feature selection (FS) is a preprocessing stage in a data mining process to reduce the dimensionality of the dataset by eliminating the redundant and irrelevant noisy features. FS is formulated as an optimization problem. Thus, metaheuristic algorithms have been proposed to find promising near optimal solutions for this complex problem. MFO is one of the recent metaheuristic algorithms which has been efficiently used to solve various optimization problems in a wide range of applications. The proposed approaches have been tested on 23 medical datasets. The comparative results revealed that the chaotic BMFO (CBMFO) significantly increased the performance of the MFO algorithm and achieved competitive results when compared with other state-of-the-arts metaheuristic algorithms.