[1]YU Zikang,DONG Hongbin.Salp swarm feature selection algorithm with a hybrid strategy[J].CAAI Transactions on Intelligent Systems,2024,19(3):757-765.[doi:10.11992/tis.202209040]
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Salp swarm feature selection algorithm with a hybrid strategy

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 3 Page number: 757-765 Column: 学术论文—人工智能基础 Public date: 2024-05-05
Title:
Salp swarm feature selection algorithm with a hybrid strategy
Author(s):
YU Zikang;  DONG Hongbin
School of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
Keywords:
feature selection; salp swarm algorithm; transient search algorithm; heuristic algorithm; mutual information; dynamic search algorithm; rank sum test; K-nearest neighbor
CLC:
TP301
DOI:
10.11992/tis.202209040
Abstract:
In recent years, with the rapid development of computer and database technologies, the number of large-scale datasets has rapidly increased. Thus, the use of feature selection technology is important to screen features with massive amounts of information. In this study, a salp swarm feature selection algorithm with a hybrid strategy (HS-SSA) is proposed. Initially, a sorted table based on mutual information is generated, and a new initialization strategy is proposed on the basis of this sorted table. Furthermore, a novel dynamic search algorithm with conditional call is proposed. With respect to location updates, the efficiency of exploration and development steps is improved, and the flexibility and diversity of the solution space are increased by combining the transient search algorithm (TSO). Consequently, the algorithm can rapidly locate the global optimal location. To verify algorithm performance, 14 UCI datasets were selected for the test. In addition, the proposed algorithm was compared with the salp swarm algorithm (SSA), the improved SSA, and many other improved algorithms in recent years. The results show that HS-SSA is more competitive in feature selection.
References:
[1] WANG Changzhong, HU Qinghua, WANG Xizhao, et al. Feature selection based on neighborhood discrimination index[J]. IEEE transactions on neural networks and learning systems, 2018, 29(7): 2986–2999.
[2] LI Xiaoping, WANG Yadi, RUIZ R. A survey on sparse learning models for feature selection[J]. IEEE transactions on cybernetics, 2022, 52(3): 1642–1660.
[3] JOHN G H, KOHAVI R, PFLEGER K. Irrelevant features and the subset selection problem[M]//Machine learning proceedings 1994. Amsterdam: Elsevier, 1994: 121-129.
[4] 董红斌, 滕旭阳, 杨雪. 一种基于关联信息熵度量的特征选择方法[J]. 计算机研究与发展, 2016, 53(8): 1684–1695
DONG Hongbin, TENG Xuyang, YANG Xue. Feature selection based on the measurement of correlation information entropy[J]. Journal of computer research and development, 2016, 53(8): 1684–1695
[5] SONG Xianfang, ZHANG Yong, GONG Dunwei, et al. Feature selection using bare-bones particle swarm optimization with mutual information[J]. Pattern recognition, 2021, 112: 107804.
[6] LI Anda, XUE Bing, ZHANG Mengjie. Improved binary particle swarm optimization for feature selection with new initialization and search space reduction strategies[J]. Applied soft computing, 2021, 106: 107302.
[7] 杨维, 李歧强. 粒子群优化算法综述[J]. 中国工程科学, 2004, 6(5): 87–94
YANG Wei, LI Qiqiang. Survey on particle swarm optimization algorithm[J]. Engineering science, 2004, 6(5): 87–94
[8] MIRJALILI S, GANDOMI A H, MIRJALILI S Z, et al. Salp swarm algorithm[J]. Advances in engineering software, 2017, 114(C): 163–191.
[9] 刘骏鹏. 哈里斯鹰算法的改进及应用研究[D]. 杭州: 浙江大学, 2021.
LIU Junpeng. Research on improvement and application of Harris eagle algorithm[D]. Hangzhou: Zhejiang University, 2021.
[10] MIRJALILI S, MIRJALILI S M, LEWIS A. Grey wolf optimizer[J]. Advances in engineering software, 2014, 69: 46–61.
[11] HOLLAND J H. Genetic algorithms[J]. Scientific American, 1992, 267(1): 66–72.
[12] KILIC F, KAYA Y, YILDIRIM S. A novel multi population based particle swarm optimization for feature selection[J]. Knowledge-based systems, 2021, 219: 106894.
[13] TU Qiang, CHEN Xuechen, LIU Xingcheng. Multi-strategy ensemble grey wolf optimizer and its application to feature selection[J]. Applied soft computing, 2019, 76(C): 16–30.
[14] MAFARJA M, ALJARAH I, HEIDARI A A, et al. Binary dragonfly optimization for feature selection using time-varying transfer functions[J]. Knowledge-based systems, 2018, 161: 185–204.
[15] TUBISHAT M, JA’AFAR S, ALSWAITTI M, et al. Dynamic salp swarm algorithm for feature selection[J]. Expert systems with applications, 2021, 164: 113873.
[16] ZAWBAA H M, EMARY E, GROSAN C, et al. Large-dimensionality small-instance set feature selection: a hybrid bio-inspired heuristic approach[J]. Swarm and evolutionary computation, 2018, 42: 29–42.
[17] ZIVKOVIC M, STOEAN C, CHHABRA A, et al. Novel improved salp swarm algorithm: an application for feature selection[J]. Sensors, 2022, 22(5): 1711.
[18] MAFARJA M M, MIRJALILI S. Hybrid whale optimization algorithm with simulated annealing for feature selection[J]. Neurocomputing, 2017, 260(C): 302–312.
[19] DHAL P, AZAD C. A multi-objective feature selection method using Newton’s law based PSO with GWO[J]. Applied soft computing, 2021, 107: 107394.
[20] QAIS M H, HASANIEN H M, ALGHUWAINEM S. Transient search optimization: a new meta-heuristic optimization algorithm[J]. Applied intelligence, 2020, 50(11): 3926–3941.
[21] SHANNON C E. A mathematical theory of communication[J]. The bell system technical journal, 1948, 27(3): 379–423.
[22] 滕旭阳, 董红斌, 孙静. 面向特征选择问题的协同演化方法[J]. 智能系统学报, 2017, 12(1): 24–31
TENG Xuyang, DONG Hongbin, SUN Jing. Co-evolutionary algorithm for feature selection[J]. CAAI transactions on intelligent systems, 2017, 12(1): 24–31
[23] LU Huijuan, CHEN Junying, YAN Ke, et al. A hybrid feature selection algorithm for gene expression data classification[J]. Neurocomputing, 2017, 256(C): 56–62.
[24] MAFARJA M, ALJARAH I, HEIDARI A A, et al. Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems[J]. Knowledge-based systems, 2018, 145(C): 25–45.
[25] DAVIE A, PATRICK M, CHRISTOPHER M, et al. UCI machine learning repository[EB/OL]. [2022–09–19].https://archive.ics.uci.edu/.
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