Enhanced Arithmetic Optimization Algorithm Guided by a Local Search for the Feature Selection Problem  

作　　者：Sana Jawarneh 

机构地区：[1]Computer Science Department,Applied College Dammam,Imam Abdulrahman Bin Faisal University,Dammam,32257,Saudi Arabia

出　　处：《Intelligent Automation & Soft Computing》2024年第3期511-525,共15页智能自动化与软计算（英文）

摘　　要：High-dimensional datasets present significant challenges for classification tasks.Dimensionality reduction,a crucial aspect of data preprocessing,has gained substantial attention due to its ability to improve classification per-formance.However,identifying the optimal features within high-dimensional datasets remains a computationally demanding task,necessitating the use of efficient algorithms.This paper introduces the Arithmetic Optimization Algorithm(AOA),a novel approach for finding the optimal feature subset.AOA is specifically modified to address feature selection problems based on a transfer function.Additionally,two enhancements are incorporated into the AOA algorithm to overcome limitations such as limited precision,slow convergence,and susceptibility to local optima.The first enhancement proposes a new method for selecting solutions to be improved during the search process.This method effectively improves the original algorithm’s accuracy and convergence speed.The second enhancement introduces a local search with neighborhood strategies(AOA_NBH)during the AOA exploitation phase.AOA_NBH explores the vast search space,aiding the algorithm in escaping local optima.Our results demonstrate that incorporating neighborhood methods enhances the output and achieves significant improvement over state-of-the-art methods.

关 键 词：Arithmetic optimization algorithm CLASSIFICATION feature selection problem optimization 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]