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- Title
A NEW HYBRID FILTER-WRAPPER FEATURE SELECTION USING EQUILIBRIUM OPTIMIZER AND SIMULATED ANNEALING.
- Authors
SHIRI, M. ANSARI; OMIDI, M. R.; MANSOURI, N.
- Abstract
Data dimensions and networks have grown exponentially with the Internet and communications. The challenge of high-dimensional data is increasing for machine learning and data science. This paper presents a hybrid filter-wrapper feature selection method based on Equilibrium Optimization (EO) and Simulated Annealing (SA). The proposed algorithm is named Filter-Wrapper Binary Equilibrium Optimizer Simulated Annealing (FWBEOSA). We used SA to solve the local optimal problem so that EO could be more accurate and better able to select the best subset of features. FWBEOSA utilizes a filtering phase that increases accuracy as well as reduces the number of selected features. The proposed method is evaluated on 17 standard UCI datasets using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers and compared with ten state-of-the-art algorithms (i.e., Binary Equilibrium Optimizer (BEO), Binary Gray Wolf Optimization (BGWO), Binary Swarm Slap Algorithm (BSSA), Binary Genetic Algorithm (BGA), Binary Particle Swarm Optimization (BPSO), Binary Social Mimic Optimization (BSMO), Binary Atom Search Optimization (BASO), Modified Flower Pollination Algorithm (MFPA), Bar Bones Particle Swarm Optimization (BBPSO) and Two-phase Mutation Gray Wolf Optimization (TMGWO)). Based on the results of the SVM classification, the highest level of accuracy was achieved in 13 out of 17 data sets (76%), and the lowest number of selected features was achieved in 15 out of 17 data sets (88%). Furthermore, the proposed algorithm using class KNN achieved the highest accuracy rate in 14 datasets (82%) and the lowest selective feature rate in 13 datasets (76%).
- Subjects
SIMULATED annealing; MATHEMATICAL models; HOMOMORPHISMS; MORPHISMS (Mathematics); MACHINE learning
- Publication
Journal of Mahani Mathematical Research Center, 2024, Vol 13, Issue 1, p293
- ISSN
2251-7952
- Publication type
Article
- DOI
10.22103/jmmr.2023.21150.1411