Improved Shuffled Binary Grasshopper Optimization Feature Selection Algorithm

doi:10.3778/j.issn.1673-9418.2005011

Abstract

Abstract:

Feature selection is to select the optimal or relatively optimal feature subsets from the original feature set of the data set to speed up classification and improve classification accuracy. An improved shuffled binary grass-hopper optimization feature selection algorithm is proposed in this paper. By introducing a binary transformation strategy that uses step size to guide individual position change, the blindness of the binary conversion is reduced, and the search performance of the algorithm in solution space is improved. By introducing shuffled complex evolution, the grasshopper population is divided into subgroups and evolved independently, which improves the diversity of algorithm and reduces the probability of premature convergence. The improved algorithm is used to select features of some data sets of UCI, and K-NN (K-nearest neighbor) classifier is used to classify and evaluate the feature subset. Experimental results show that compared with the basic binary grasshopper optimization algorithm, binary particle swarm optimization algorithm and binary gray wolf optimization algorithm, the improved algorithm has better search performance, convergence performance and strong robustness, and can obtain better feature subsets and better classification effect.

Key words: binary, grasshopper optimization algorithm, shuffled complex evolution, feature selection, classification, K-nearest neighbor (K-NN) algorithm

摘要：

特征选择是从数据集的原始特征中选出最优或较优特征子集，从而在加快分类速度的同时提高分类准确率。提出了一种改进的混合二进制蝗虫优化特征选择算法：通过引入步长引导个体位置变化的二进制转化策略，降低了进制转换的盲目性，提高了算法在解空间中的搜索性能；通过引入混合复杂进化方法，将蝗虫群体划分子群并独立进化，提高了算法的多样性，降低了早熟收敛的概率。采用改进算法对UCI部分数据集进行特征选择，使用K-NN分类器对特征子集进行分类评价，实验结果表明：与基本二进制蝗虫优化算法、二进制粒子群优化算法和二进制灰狼优化算法相比，改进算法具有较优的搜索性能、收敛性能与较强的鲁棒性，能够获得更好的特征子集，取得更好的分类效果。

关键词: 二进制, 蝗虫优化算法, 混合复杂进化方法, 特征选择, 分类, [K]邻近（K-NN）算法

ZHAO Zeyuan, DAI Yongqiang. Improved Shuffled Binary Grasshopper Optimization Feature Selection Algorithm[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(7): 1339-1349.

赵泽渊, 代永强. 改进混合二进制蝗虫优化特征选择算法[J]. 计算机科学与探索, 2021, 15(7): 1339-1349.

References

[1] SAEYS Y, INZA I, LARRA?AGA P, et al. A review of feature selection techniques in bioinformatics[J]. Bioinformatics, 2007, 23(19): 2507-2517.
[2] ZHANG Y D, HUO Y K, WU L N, et al. A review of dim-ension reduction techniques and methods[J]. Journal of Sichuan Ordnance, 2010, 31(10): 1-7.
张煜东, 霍元铠, 吴乐南, 等. 降维技术与方法综述[J]. 四川兵工学报, 2010, 31(10): 1-7.
[3] LI M, KAMIL M. Research on feature selection method and algorithm[J]. Computer Technology and Development, 2013, 23(12): 16-21.
李敏, 卡米力·木依丁. 特征选择方法与算法的研究[J]. 计算机技术与发展, 2013, 23(12): 16-21.
[4] HAMEDMOGHADAM H, JALILI M, YU X. An opinion formation based binary optimization approach for feature selection[J]. Physica A Statistical Mechanics & Its Applications, 2018, 491: 142-152.
[5] WEI J, ZHANG R, YU Z, et al. A BPSO-SVM algorithm based on memory renewal and enhanced mutation mechanisms for feature selection[J]. Applied Soft Computing, 2017, 58(1): 176-192.
[6] GHEYAS I A, SMITH L S. Feature subset selection in large dimensionality domains[J]. Pattern Recognition, 2010, 43(1): 5-13.
[7] LIU H, MOTODA H. Computational methods of feature sele-ction[M]. Boca Raton: CRC Press, 2007.
[8] ZHANG Q W, WEI Y C. Particle swarm optimization with independent adaptive parameter adjustment[J]. Journal of Frontiers of Computer Science and Technology, 2020, 14(4): 637-648.
张其文, 尉雅晨. 独立自适应调整参数的粒子群优化算法[J]. 计算机科学与探索, 2020, 14(4): 637-648.
[9] ZHANG D H, YOU X M, LIU S. Dynamic grouping ant colony algorithm combined with cat swarm optimization[J]. Journal of Frontiers of Computer Science and Technology, 2020, 14(5): 880-891.
张德惠, 游晓明, 刘升. 融合猫群算法的动态分组蚁群算法[J]. 计算机科学与探索, 2020, 14(5): 880-891.
[10] ZHANG Z C, LIU S Y. Firefly algorithm based on topology improvement and crossover strategy[J]. Computer Engineering and Applications, 2019, 55(7): 1-8.
张哲辰, 刘三阳. 基于拓扑改进与交叉策略的萤火虫算法[J]. 计算机工程与应用, 2019, 55(7): 1-8.
[11] YANG J, HONAVAR V. Feature subset selection using a genetic algorithm[J]. IEEE Intelligent Systems, 1998, 13(2): 44-49.
[12] SREEJA N K, SANKAR A. Pattern matching based classification using ant colony optimization based feature selection[J]. Applied Soft Computing, 2015, 31: 91-102.
[13] VIEIRA S M, MENDON?A L F, FARINHA G J, et al. Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients[J]. Applied Soft Computing, 2013, 13(8): 3494-3504.
[14] SAREMI S, MIRJALILI S, LEWIS A. Grasshopper optimization algorithm: theory and application[J]. Advances in Engineering Software, 2017, 105: 30-47.
[15] TUMULURU P, RAVI B. GOA-based DBN: grasshopper optimization algorithm-based deep belief neural networks for cancer classification[J]. International Journal of Applied Engineering Research, 2017, 12(24): 14218-14231.
[16] 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: 24-45.
[17] LIAO L, ZHOU Y. A neighborhood centroid opposition-based grasshopper optimization algorithm[J]. Journal of Physics Conference, 2019, 1176(3): 32-44.
[18] FATHY A. Recent meta-heuristic grasshopper optimization algorithm for optimal reconfiguration of partially shaded PV array[J]. Solar Energy, 2018, 171: 638-651.
[19] LAL D, BARISAL A. Grasshopper algorithm optimized fra-ctional order fuzzy PID frequency controller for hybrid power systems[J]. Recent Advances in Electrical & Electronic Engineering, 2019, 12(6): 519-531.
[20] YAN X, YE C M. Grasshopper optimization algorithm for job-shop scheduling problem[J]. Computer Engineering and Applications, 2019, 55(6): 257-264.
闫旭, 叶春明. 混合蝗虫优化算法求解作业车间调度问题[J]. 计算机工程与应用, 2019, 55(6): 257-264.
[21] LI Y Z, GU L. Grasshopper optimization algorithm based on curve adaptive and simulated annealing[J]. Application Research of Computers, 2019, 36(12): 3637-3643.
李洋州, 顾磊. 基于曲线自适应和模拟退火的蝗虫优化算法[J]. 计算机应用研究, 2019, 36(12): 3637-3643.
[22] DUAN Q Y, GUPTA V K, SOROOSHIAN S. Shuffled complex evolution approach for effective and efficient global minimi-zation[J]. Journal of Optimization Theory & Applications, 1993, 76(3): 501-521.
[23] HUANG X, MO H M, ZHAO Z G, et al. Research on discrete enhanced fireworks algorithm and kNN in feature selection[J]. Computer Engineering and Applications, 2020, 56(16): 112-117.
黄欣, 莫海淼, 赵志刚, 等. 离散型增强烟花算法和kNN在特征选择中的研究[J]. 计算机工程与应用, 2020, 56(16): 112-117.
[24] BHARTI K K, SINGH P K. Opposition chaotic fitness mutation based adaptive inertia weight BPSO for feature selection in text clustering[J]. Applied Soft Computing, 2016, 43: 20-34.
[25] EMARY E, ZAWBAA H M, HASSANIEN A E. Binary grey wolf optimization approaches for feature selection[J]. Neuro-computing, 2016, 172: 371-381.