Dynamic Individual Selection and Crossover Boosted Forensic-based Investigation Algorithm for Global Optimization and Feature Selection

作     者：Hanyu Hu Weifeng Shan Jun Chen Lili Xing Ali Asghar Heidari Huiling Chen Xinxin He Maofa Wang Hanyu Hu;Weifeng Shan;Jun Chen;Lili Xing;Ali Asghar Heidari;Huiling Chen;Xinxin He;Maofa Wang

作者机构：School of Emergency ManagementInstitute of Disaster PreventionSanhe065201China Earthquake Administration of Anhui ProvinceHefei230031China Department of Computer Science and Artificial IntelligenceWenzhou UniversityWenzhou325035China Guangxi Key Laboratory of Trusted SoftwareGuilin University of Electronic TechnologyGuilin541004China 

出 版 物：《Journal of Bionic Engineering》 (仿生工程学报(英文版))

年 卷 期：2023年第20卷第5期

页      面：2416-2442页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 081202[工学-计算机软件与理论] 

基　　金：supported by Special Fund of Fundamental Scientific Research Business Expense for Higher School of Central Government(ZY20180119) the Natural Science Foundation of Zhejiang Province(LZ22F020005) the Natural Science Foundation of Hebei Province(D2022512001) National Natural Science Foundation of China(42164002,62076185). 

主　　题：Feature selection Forensic-based investigation algorithm Crisscross mechanism Global optimization Metaheuristic algorithms Bionic algorithm 

摘      要：The advent of Big Data has rendered Machine Learning tasks more intricate as they frequently involve higher-dimensional data.Feature Selection(FS)methods can abate the complexity of the data and enhance the accuracy,generalizability,and interpretability of models.Meta-heuristic algorithms are often utilized for FS tasks due to their low requirements and efficient performance.This paper introduces an augmented Forensic-Based Investigation algorithm(DCFBI)that incorporates a Dynamic Individual Selection(DIS)and crisscross(CC)mechanism to improve the pursuit phase of the FBI.Moreover,a binary version of DCFBI(BDCFBI)is applied to FS.Experiments conducted on IEEE CEC 2017 with other metaheuristics demonstrate that DCFBI surpasses them in search capability.The influence of different mechanisms on the original FBI is analyzed on benchmark functions,while its scalability is verified by comparing it with the original FBI on benchmarks with varied dimensions.BDCFBI is then applied to 18 real datasets from the UCI machine learning database and the Wieslaw dataset to select near-optimal features,which are then compared with six renowned binary metaheuristics.The results show that BDCFBI can be more competitive than similar methods and acquire a subset of features with superior classification accuracy.