Journal of Bionic Engineering ›› 2023, Vol. 20 ›› Issue (5): 2416-2442.doi: 10.1007/s42235-023-00367-5
Dynamic Individual Selection and Crossover Boosted Forensic‑based Investigation Algorithm for Global Optimization and Feature Selection
Hanyu Hu1;Weifeng Shan1;Jun Chen2;Lili Xing1;Ali Asghar Heidari3; Huiling Chen3; Xinxin He1;Maofa Wang4
- 1 School of Emergency Management, Institute of Disaster Prevention, Sanhe 065201, China 2 Earthquake Administration of Anhui Province, Hefei 230031, China 3 Department of Computer Science and Artifcial Intelligence, Wenzhou University, Wenzhou 325035, China 4 Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China
Dynamic Individual Selection and Crossover Boosted Forensic‑based Investigation Algorithm for Global Optimization and Feature Selection
Hanyu Hu1;Weifeng Shan1;Jun Chen2;Lili Xing1;Ali Asghar Heidari3; Huiling Chen3; Xinxin He1;Maofa Wang4
- 1 School of Emergency Management, Institute of Disaster Prevention, Sanhe 065201, China 2 Earthquake Administration of Anhui Province, Hefei 230031, China 3 Department of Computer Science and Artifcial Intelligence, Wenzhou University, Wenzhou 325035, China 4 Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China
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. The results show that BDCFBI can be more competitive than similar methods and acquire a subset of features with superior classification accuracy.