A Robust Adaptive Hierarchical Learning Crow Search Algorithm for Feature Selection

被引:9
作者
Chen, Yilin [1 ,2 ]
Ye, Zhi [1 ]
Gao, Bo [1 ]
Wu, Yiqi [3 ]
Yan, Xiaohu [4 ]
Liao, Xiangyun [5 ]
机构
[1] Wuhan Inst Technol, Sch Comp Sci & Engn, Wuhan 430073, Peoples R China
[2] Wuhan Inst Technol, Hubei Key Lab Intelligent Robot, Wuhan 430073, Peoples R China
[3] China Univ Geosci, Sch Comp Sci, Wuhan 100083, Peoples R China
[4] Shenzhen Polytech, Sch Artificial Intelligence, Shenzhen 518055, Peoples R China
[5] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China
基金
中国国家自然科学基金;
关键词
feature selection; crow search algorithm; hierarchical learning; information sharing; multi-strategy; GRASSHOPPER OPTIMIZATION ALGORITHM;
D O I
10.3390/electronics12143123
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Feature selection is a multi-objective problem, which can eliminate irrelevant and redundant features and improve the accuracy of classification at the same time. Feature selection is a great challenge to balance the conflict between the two goals of selection accuracy and feature selection ratio. The evolutionary algorithm has been proved to be suitable for feature selection. Recently, a new meta-heuristic algorithm named the crow search algorithm has been applied to the problem of feature selection. This algorithm has the advantages of few parameters and achieved good results. However, due to the lack of diversity in late iterations, the algorithm falls into local optimal problems. To solve this problem, we propose the adaptive hierarchical learning crow search algorithm (AHL-CSA). Firstly, an adaptive hierarchical learning technique was used to adaptive divide the crow population into several layers, with each layer learning from the top layer particles and the topmost layer particles learning from each other. This strategy encourages more exploration by lower individuals and more exploitation by higher individuals, thus improving the diversity of the population. In addition, in order to make full use of the search information of each level in the population and reduce the impact of local optimization on the overall search performance of the algorithm, we introduce an information sharing mechanism to help adjust the search direction of the population and improve the convergence accuracy of the algorithm. Finally, different difference operators are used to update the positions of particles at different levels. The diversity of the population is further improved by using different difference operators. The performance of the method was tested on 18 standard UCI datasets and compared with eight other representative algorithms. The comparison of experimental results shows that the proposed algorithm is superior to other competitive algorithms. Furthermore, the Wilcoxon rank-sum test was used to verify the validity of the results.
引用
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页数:20
相关论文
共 51 条
[1]   Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection [J].
Al-Tashi, Qasem ;
Kadir, Said Jadid Abdul ;
Rais, Helmi Md ;
Mirjalili, Seyedali ;
Alhussian, Hitham .
IEEE ACCESS, 2019, 7 :39496-39508
[2]   Improving grasshopper optimization algorithm for hyperparameters estimation and feature selection in support vector regression [J].
Algamal, Zakariya Yahya ;
Qasim, Maimoonah Khalid ;
Lee, Muhammad Hisyam ;
Ali, Haithem Taha Mohammad .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2021, 208
[3]  
Bangyal WH, 2019, INT J ADV COMPUT SC, V10, P481
[4]  
Bs A., 2020, INFORM SCIENCES, V530, P22
[5]   Feature selection using Binary Crow Search Algorithm with time varying flight length [J].
Chaudhuri, Abhilasha ;
Sahu, Tirath Prasad .
EXPERT SYSTEMS WITH APPLICATIONS, 2021, 168
[6]   Deep-space trajectory optimizations using differential evolution with self-learning [J].
Choi, Jin Haeng ;
Lee, Jinah ;
Park, Chandeok .
ACTA ASTRONAUTICA, 2022, 191 :258-269
[7]   An optimization method for chaotic turbulent flow [J].
Chung, Seung Whan ;
Freund, Jonathan B. .
JOURNAL OF COMPUTATIONAL PHYSICS, 2022, 457
[8]  
Demsar J, 2006, J MACH LEARN RES, V7, P1
[9]   Dynamic behaviours on oxidation heat release of key active groups for coal with different degrees of metamorphism [J].
Deng, Jun ;
Hu, Peng ;
Bai, Zu-Jin ;
Wang, Cai-Ping ;
Kang, Fu-Ru ;
Liu, Le .
FUEL, 2022, 320
[10]   Solving Fuzzy Job-Shop Scheduling Problem Using DE Algorithm Improved by a Selection Mechanism [J].
Gao, Da ;
Wang, Gai-Ge ;
Pedrycz, Witold .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2020, 28 (12) :3265-3275