Hybrid Filter-Wrapper Feature Selection using Modified Flower Pollination Algorithm

Computer and Knowledge Engineering

مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 07 تیر 1404   

نوع مقاله: Original Article

شناسه دیجیتال (DOI): 10.22067/cke.2025.89191.1124

نویسندگان

Mohammad Ansari Shiri¹؛ najme mansouri^{* 2}

¹Department of Computer Science, Shahid Bahonar University of Kerman

²shahid ba honar university of kerman

چکیده

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.

کلیدواژه‌ها

Feature selection؛ Flower Pollination Algorithm؛ Filter؛ Wrapper