hybrid filter-wrapper feature selection using modified flower pollination algorithm

A major challenge in machine learning and data science is feature selection. feature selection involves selecting the optimal (or suboptimal) subset of features to derive useful conclusions from a dataset based on the relevant information contained in those features. flower pollination algorithm (fpa) is a metaheuristic algorithm developed recently based on flower pollination. in this paper, we propose a new type of binary fpa, called the filter-wrapper modified binary fpa (fwmbfpa), which aims to improve convergence rate and solution quality by combining filter and wrapper advantages. using fwmbfpa, the exploration process is directed towards specific search areas by extracting the features of existing solutions. 18 uci datasets are used to evaluate the performance of the method. fwmbfpa generally performs better than the other algorithms in terms of average classification accuracy. fwmbfpa achieves highest classification accuracy with the smallest number of selected features when compared to other algorithms when dealing with datasets with a large number of features.