TY - JOUR
KW - Optimization
KW - Classification
KW - Feature Selection
KW - Machine Learning Classifier
AU - Lahbib Khrissi
AU - Nabil El Akkad
AU - Hassan Satori
AU - Khalid Satori
AB - Feature selection is an active research area in data mining and machine learning, especially with the increase in the amount of numerical data. FS is a search strategy to find the best subset of features among a large number of subsets of features. Thus, FS is applied in most modern applications and in various domains, which requires the search for a powerful FS technique to process and classify high-dimensional data. In this paper, we propose a new technique for dimension reduction in feature selection. This approach is based on a recent metaheuristic called Archimedes’ Optimization Algorithm (AOA) to select an optimal subset of features to improve the classification accuracy. The idea of the AOA is based on the steps of Archimedes' principle in physics. It explains the behavior of the force exerted when an object is partially or fully immersed in a fluid. AOA optimization maintains a balance between exploration and exploitation, keeping a population of solutions and studying a large area to find the best overall solution. In this study, AOA is exploited as a search technique to find an optimal feature subset that reduces the number of features to maximize classification accuracy. The K-nearest neighbor (K-NN) classifier was used to evaluate the classification performance of selected feature subsets. To demonstrate the superiority of the proposed method, 16 benchmark datasets from the UCI repository are used and also compared by well-known and recently introduced meta-heuristics in this context, such as: sine-cosine algorithm (SCA), whale optimization algorithm (WOA), butterfly optimization algorithm (BAO), and butterfly flame optimization algorithm (MFO). The results prove the effectiveness of the proposed algorithm over the other algorithms based on several performance measures used in this paper.
IS - In Press
M1 - In Press
N2 - Feature selection is an active research area in data mining and machine learning, especially with the increase in the amount of numerical data. FS is a search strategy to find the best subset of features among a large number of subsets of features. Thus, FS is applied in most modern applications and in various domains, which requires the search for a powerful FS technique to process and classify high-dimensional data. In this paper, we propose a new technique for dimension reduction in feature selection. This approach is based on a recent metaheuristic called Archimedes’ Optimization Algorithm (AOA) to select an optimal subset of features to improve the classification accuracy. The idea of the AOA is based on the steps of Archimedes' principle in physics. It explains the behavior of the force exerted when an object is partially or fully immersed in a fluid. AOA optimization maintains a balance between exploration and exploitation, keeping a population of solutions and studying a large area to find the best overall solution. In this study, AOA is exploited as a search technique to find an optimal feature subset that reduces the number of features to maximize classification accuracy. The K-nearest neighbor (K-NN) classifier was used to evaluate the classification performance of selected feature subsets. To demonstrate the superiority of the proposed method, 16 benchmark datasets from the UCI repository are used and also compared by well-known and recently introduced meta-heuristics in this context, such as: sine-cosine algorithm (SCA), whale optimization algorithm (WOA), butterfly optimization algorithm (BAO), and butterfly flame optimization algorithm (MFO). The results prove the effectiveness of the proposed algorithm over the other algorithms based on several performance measures used in this paper.
PY - 9998
SE - 1
SP - 1
EP - 10
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - A Feature Selection Approach Based on Archimedes’ Optimization Algorithm for Optimal Data Classification
UR - https://www.ijimai.org/journal/sites/default/files/2023-01/ip2023_01_005.pdf
VL - In Press
SN - 1989-1660
ER -