Rank Based Moth Flame optimisation for Feature Selection in the Medical Application
Ruba Abu Khurma, Ibrahim Aljarah, Ahmad Sharieh · 2020
Feature selection (FS) is a challenging data mining problem that incorporates a complex search process to find the most informative feature subset. In the brute force methods generating the entire feature space and applying an exhaustive search makes the FS NP-hard problem. Meta-heuristic algorithms are good alternative solutions that provide (near) optimal solutions through a random search process instead of a complete search. In this paper, an FS approach based on the Moth Flame optimization algorithm (MFO) and k-NN classifier are proposed. MFO is a recent meta-heuristic algorithm that has proved its effectiveness in solving different complex problems in a reasonable time. Nevertheless, the performance of MFO highly depends on achieving a balance between exploration and exploitation during the search process. To address this issue, we propose an adaptive method to update the position of a moth toward the best global solution based on the search status. The proposed MFO has been evaluated using sixteen benchmark medical data sets and the results show promising performance of the modified MFO algorithm in terms of the applied evaluation measures.