Multi-Label Feature Selection Using a Hybrid Approach Based on the Particle Swarm Optimization Algorithm
Subject Areas : electrical and computer engineeringَAzar Rafiei 1 , Parham Moradi 2 , Abdolbaghi Ghaderzadeh 3
1 - Azad University
2 -
3 - Azad University
Keywords: Multi-label classification, feature selection, swarm intelligence local search strategy, particle swarm optimization,
Abstract :
Multi-label classification is one of the important issues in machine learning. The efficiency of multi-label classification algorithms decreases drastically with increasing problem dimensions. Feature selection is one of the main solutions for dimension reduction in multi-label problems. Multi-label feature selection is one of the NP solutions, and so far, a number of solutions based on collective intelligence and evolutionary algorithms have been proposed for it. Increasing the dimensions of the problem leads to an increase in the search space and consequently to a decrease in efficiency and also a decrease in the speed of convergence of these algorithms. In this paper, a hybrid collective intelligence solution based on a binary particle swarm optimization algorithm and local search strategy for multi-label feature selection is presented. To increase the speed of convergence, in the local search strategy, the features are divided into two categories based on the degree of extension and the degree of connection with the output of the problem. The first category consists of features that are very similar to the problem class and less similar to other features, and the second category is similar features and less related. Therefore, a local operator is added to the particle swarm optimization algorithm, which leads to the reduction of irrelevant features and extensions of each solution. Applying this operator leads to an increase in the convergence speed of the proposed algorithm compared to other algorithms presented in this field. The performance of the proposed method has been compared with the most well-known feature selection methods on different datasets. The results of the experiments showed that the proposed method has a good performance in terms of accuracy.
[1] م. رحمانی¬نیا و پ. مرادی، "يك الگوريتم انتخاب ويژگي برخط در جريان داده¬ها با استفاده از اطلاعات متقابل چندمتغيره،" نشریه مهندسی برق و مهندسی كامپیوتر ایران، ب- مهندسی کامپیوتر، سال 18، شماره 4، صص. 336-327، زمستان 1399.
[2] Y. Lin, Q. Hu, J. Liu, J. Chen, and J. Duan, "Multi-label feature selection based on neighborhood mutual information," Applied Soft Computing, vol. 38, pp. 244-256, Jan. 2016.
[3] O. Reyes, C. Morell, and S. Ventura, "Scalable extensions of the ReliefF algorithm for weighting and selecting features on the multi-label learning context," Neurocomputing, vol. 161, pp. 168-182, Aug. 2015.
[4] L. Li, et al., "Multi-label feature selection via information gain," in Proc. Int. Conf. on Advanced Data Mining and Applications, ADMA'14, pp. 345-355, Guilin, China, 19-21 Dec. 2014.
[5] Y. Lin, Q. Hu, J. Liu, and J. Duan, "Multi-label feature selection based on max-dependency and min-redundancy," Neurocomputing, vol. 168, pp. 92-103, Nov. 2015.
[6] S. Tabakhi and P. Moradi, "Relevance-redundancy feature selection based on ant colony optimization," Pattern Recognition, vol. 48, no. 9, pp. 2798-2811, Sept. 2015.
[7] P. Moradi and M. Rostami, "Integration of graph clustering with ant colony optimization for feature selection," Knowledge-Based Systems, vol. 84, pp. 144-161, Aug. 2015.
[8] J. Lee and D. W. Kim, "Memetic feature selection algorithm for multi-label classification," Information Sciences, vol. 293, pp. 80-96, Feb. 2015.
[9] Y. Yu and Y. Wang, "Feature selection for multi-label learning using mutual information and GA," in Proc. 9th Int. Conf. on Rough Sets and Knowledge Technology, RSKT'14, pp. 454-463, Shanghai, China, 24-26 Oct. 2014.
[10] Y. Zhang, D. W. Gong, X. Y. Sun, and Y. N. Guo, "A PSO- based multi-objective multi-label feature selection method in classification," Scientific Reports, vol. 7, Article ID: 376, Mar. 2017.
[11] M. L. Zhang, J. M. Peña, and V. Robles, "Feature selection for multi-label naive bayes classification," Information Sciences, vol. 179, no. 19, pp. 3218-3229, Sept. 2009.
[12] M. A. Khan, A. Ekbal, E. L. Mencía, and J. Fürnkranz, "Multi-objective optimisation-based feature selection for multi-label classification," in Proc. Int. Conf. on Applications of Natural Language to Information Systems, NLDB'17, pp. 38-41, Liege, Belgium, 21-23 Jun. 2017.
[13] M. You, J. Liu, G. Z. Li, and Y. Chen, "Embedded feature selection for multi-label classification of music emotions," International J. of Computational Intelligence Systems, vol. 5, no. 4, pp. 668-678, Aug. 2012.
[14] P. Zhu, Q. Xu, Q. Hu, C. Zhang, and H. Zhao, "Multi-label feature selection with missing labels," Pattern Recognition, vol. 74, pp. 488-502, Feb. 2018.
[15] S. Tabakhi, A. Najafi, R. Ranjbar, and P. Moradi, "Gene selection for microarray data classification using a novel ant colony optimization," Neurocomputing, vol. 168, pp. 1024-1036, Nov. 2015.
[16] R. K. Sivagaminathan and S. Ramakrishnan, "A hybrid approach for feature subset selection using neural networks and ant colony optimization," Expert Systems with Applications, vol. 33, no. 1, pp. 49-60, Jul. 2007.
[17] M. H. Aghdam, N. Ghasem-Aghaee, and M. E. Basiri, "Text feature selection using ant colony optimization," Expert Systems with Applications, vol. 36, no. 3, pt. 2, pp. 6843-6853, Apr. 2009.
[18] M. Paniri, M. B. Dowlatshahi, and H. Nezamabadi-pour, "MLACO: a multi-label feature selection algorithm based on ant colony optimization," Knowledge-Based Systems, vol. 192, Article ID: 105285, Mar. 2020.
[19] J. Yang and V. Honavar, "Feature subset selection using a genetic algorithm," IEEE Intelligent Systems, vol. 13, no. 2, pp. 117-136, Mar. 1998.
[20] M. Rostami and P. Moradi, "A clustering based genetic algorithm for feature selection," in Proc. 6th Conf. on Information and Knowledge Technology, IKT'14, pp. 112-116, Shahrood, Iran, 27-29 May. 2014.
[21] T. M. Hamdani, J. M. Won, A. M. Alimi, and F. Karray, "Hierarchical genetic algorithm with new evaluation function and bi-coded representation for the selection of features considering their confidence rate," Applied Soft Computing, vol. 11, no. 2, pp. 2501-2509, Mar. 2011.
[22] S. W. Lin, Z. J. Lee, S. C. Chen, and T. Y. Tseng, "Parameter determination of support vector machine and feature selection using simulated annealing approach," Applied Soft Computing, vol. 8, no. 4, pp. 1505-1512, Sep. 2008.
[23] S. W. Lin, T. Y. Tseng, S. Y. Chou, and S. C. Chen, "A simulated-annealing-based approach for simultaneous parameter optimization and feature selection of back-propagation networks," Expert Systems with Applications, vol. 34, no. 2, pp. 1491-1499, Feb. 2008.
[24] L. Y. Chuang, C. H. Yang, and J. C. Li, "Chaotic maps based on binary particle swarm optimization for feature selection," Applied Soft Computing, vol. 11, no. 1, pp. 239-248, Jan. 2011.
[25] Y. Liu, et al., "An improved particle swarm optimization for feature selection," J. of Bionic Engineering, vol. 8, no. 2, pp. 191-200, Jun. 2011.
[26] B. Xue, M. Zhang, and W. N. Browne, "Particle swarm optimisation for feature selection in classification: novel initialisation and updating mechanisms," Applied Soft Computing, vol. 18, pp. 261-276, May 2014.
[27] H. M. Abdelsalam and A. M. Mohamed, "Optimal sequencing of design projects' activities using discrete particle swarm optimisation," International J. of Bio-Inspired Computation, vol. 4, no. 2, pp. 100-110, 2012.
[28] K. Demir, B. H. Nguyen, B. Xue, and M. Zhang, " Particle swarm optimisation for sparsity-based feature selection in multi-label classification," in Proc. of the Genetic and Evolutionary Computation Conf. Companion, pp. 232-235, Boston, MA, USA, 9-13 Jul. 2022.
[29] J. Lee and D. W. Kim, "Mutual information-based multi-label feature selection using interaction information," Expert Systems with Applications, vol. 42, no. 4, pp. 2013-2025, Mar. 2015.
[30] W. Chen, J. Yan, B. Zhang, Z. Chen, and Q. Yang, "Document transformation for multi-label feature selection in text categorization," in Proc of 7th IEEE Int. Conf. on Data Mining, ICDM'07, vol. ???, pp. 451-456, Omaha, NE, USA, 28-31 Oct. 2007.
[31] N. Spolaôr, E. A. Cherman, M. C. Monard, and H. D. Lee, "A comparison of multi-label feature selection methods using the problem transformation approach," Electronic Notes in Theoretical Computer Science, vol. 292, pp. 135-151, Mar. 2013.
[32] G. Doquire and M. Verleysen, "Feature selection for multi-label classification problems," in Proc of Int. Work-Conf. on Artificial Neural Networks, IWANN'11, pp. 9-16, Torremolinos-Málaga, Spain, 8-10 Jun. 2011.
[33] G. Doquire and M. Verleysen, "Mutual information-based feature selection for multilabel classification," Neurocomputing, vol. 122, pp. 148-155, Dec. 2013.
[34] J. Lee and D. W. Kim, "Fast multi-label feature selection based on information-theoretic feature ranking," Pattern Recognition, vol. 48, no. 9, pp. 2761-2771, Sept. 2015.
[35] J. Read, B. Pfahringer, and G. Holmes, "Multi-label classification using ensembles of pruned sets," in Proc of 8th IEEE Int. Conf. on Data Mining, pp. 995-1000, Pisa, Italy, 15-19 Dec. 2008.
[36] A. Hashemi, M. B. Dowlatshahi, and H. Nezamabadi-pour, "MGFS: a multi-label graph-based feature selection algorithm via PageRank centrality," Expert Systems with Applications, vol. 142, Article ID: 113024, Mar. 2020.
[37] Z. Sun, et al., "Mutual information based multi-label feature selection via constrained convex optimization," Neurocomputing, vol. 329, pp. 447-456, Feb. 2019.
[38] J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proc. of Int. Conf. on Neural Networks, ICNN'95, vol. 4, pp. 1942-1948, Perth, Australia, 27 Nov.-1 Dec. 1995.
[39] ح. افراخته و پ. مرادی، "روشی جدید بهمنظور خوشهبندی دادههای سرعت باد در نیروگاههای بادی با استفاده از الگوریتمهای FCM و PSO ،" نشریه مهندسی برق و مهندسی كامپیوتر ایران، ب- مهندسی کامپیوتر، سال 8، شماره 3، صص. 214-210، پاییز 1389.
[40] M. M. Kabir, M. Shahjahan, and K. Murase, "A new local search based hybrid genetic algorithm for feature selection," Neurocomputing, vol. 74, no. 17, pp. 2914-2928, Oct. 2011.
[41] D. P. Muni, N. R. Pal, and J. Das, Genetic Programming for Simultaneous Feature Selection and Classifier Design, 2006.
[42] M. M. Kabir, M. M. Islam, and K. Murase, "A new wrapper feature selection approach using neural network," Neurocomputing, vol. 73, pp. 3273-3283, Oct. 2010.
[43] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, "GroupLens: an open architecture for collaborative filtering of netnews," in Proc. of the ACM Conf. on Computer Supported Cooperative Work, CSCW'94, pp. 175-186, Chapel Hill, NC, USA, 22-26 Oct. 1994.
[44] X. He, D. Cai, and P. Niyogi, "Laplacian score for feature selection," in Proc. of the 18th Int. Conf. on Neural Information Processing Systems, NIPS'05, pp. 507-514, Vancouver, Canada, 5-8 Dec. 2005.
[45] M. Stone, "Cross‐validatory choice and assessment of statistical predictions," J. of the Royal Statistical Society: Series B (Methodological), vol. 36, pp. 111-133, 1974.
[46] M. L. Zhang and Z. H. Zhou, "ML-KNN: a lazy learning approach to multi-label learning," Pattern Recognition, vol. 40, no. 7, pp. 2038-2048, Jul. 2007.
[47] S. Kashef and H. Nezamabadi-pour, "A label-specific multi-label feature selection algorithm based on the Pareto dominance concept," Pattern Recognition, vol. 88, pp. 654-667, 2019.
[48] J. Lee and D. W. Kim, "Feature selection for multi-label classification using multivariate mutual information," Pattern Recognition Letters, vol. 34, no. 3, pp. 349-357, Feb. 2013.
[49] D. J. Sheskin, Handbook of Parametric and Nonparametric Statistical Procedures, 5th ed., Chapman & Hall, 2011.
