Abstract
Feature selection is an important data preprocessing technique in the emerging field of artificial intelligence and data mining which aims at finding a small set of features from the original dataset with predetermined targets. Particle swarm optimization (PSO) has been widely used to address feature selection problems because of its easy implementation, efficiency and simplicity. However, in high-dimensional problems, selecting the discriminative features with a higher correct classification rate is limited. To solve the issue above, a particle swarm optimization method with adaptive mechanism and new updating strategy is proposed to choose best features to improve the correct classification rate. The proposed approach, named as EPSO, is verified and compared with other three meta-heuristic algorithms and four recent PSO-based feature selection methods. The experimental results and statistical tests have proved the efficiency and feasibility of the EPSO approach in obtaining higher classification accuracy along with smaller number of features. Therefore, the proposed EPSO algorithm can be successfully used as a novel feature selection strategy.
Supported by The National Key R & D Program of China (Grant No. 2017YFB1302400), National Natural Science Foundation of China (Grant No. 61773242 and 61803227), Key Program of Scientific and Technological Innovation of Shandong Province (Grand No. 2017CXGC0926), Key Research and Development Program of Shandong Province (Grant No. 2017GGX30133).
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References
Bhopale, A.-P., Kamath, S.-S., Tiwari, A.: Concise semantic analysis based text categorization using modified hybrid union feature selection approach. In: 4th International Conference on Recent Advances in Information Technology, pp. 1–7. IEEE (2018)
Lin, S.-D., Wang, D.-E.: Features selection and statistical classification for pose-invariant face recognition. In: 10th International Conference on Advanced Computational Intelligence, pp. 23–27. IEEE (2018)
Ragone, A., Tomeo, P., Magarelli, C., Noia, T.-D.: Schema-summarization in linked-data-based feature selection for recommender systems. In: Proceedings of the Symposium on Applied Computing, pp. 330–335. ACM (2017)
Xue, B., Cervante, L., Shang, L., Browne, W.-N., Zhang, M.-J.: A multi-objective particle swarm optimisation for filter-based feature selection in classification problems. Connect. Sci. 24(2–3), 91–116 (2012)
Kohavi, R., John, G.-H.: Wrappers for feature subset selection. Artif. Intell. 97(1–2), 273–324 (1997)
Nguyen, H.B., Xue, B., Andreae, P.: Mutual information estimation for filter based feature selection using particle swarm optimization. In: Squillero, G., Burelli, P. (eds.) EvoApplications 2016. LNCS, vol. 9597, pp. 719–736. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31204-0_46
Nguyen, B.H., Xue, B., Andreae, P.: A novel binary particle swarm optimization algorithm and its applications on knapsack and feature selection problems. In: Leu, G., Singh, H.K., Elsayed, S. (eds.) Intelligent and Evolutionary Systems. PALO, vol. 8, pp. 319–332. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-49049-6_23
Ghamisi, P., Benediktsson, J.-A.: Feature selection based on hybridization of genetic algorithm and particle swarm optimization. IEEE Geosci. Remote Sens. Lett. 12(2), 309–313 (2015)
Nguyen, H.-B., Xue, B., Andreae, P., Zhang, M.-J.: Particle swarm optimisation with genetic operators for feature selection. In: CEC 2017, pp. 286–293. IEEE (2017)
Moradi, P., Gholampour, M.: A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy. Appl. Soft Comput. 43, 117–130 (2016)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE (1995)
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: IEEE World Congress on Computational Intelligence, pp. 69–73. IEEE (1998)
Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evolut. Comput. 6(1), 58–73 (2002)
Sindhu, R., Ngadiran, R., Yacob, Y.-M., Zahri, N.-A.-H., Hariharn, M.: Sine–cosine algorithm for feature selection with elitism strategy and new updating mechanism. Neural Comput. Appl. 28(10), 2947–2958 (2017)
Hancer, E., Xue, B., Zhang, M.-J., Karaboga, D., Akay, B.: Pareto front feature selection based on artificial bee colony optimization. Inf. Sci. 422, 462–479 (2018)
Newman, S.-H.-D.-J., Blake, C.-L., Merz, C.-L.: UCI Repository of Machine Learning Databases (1998). http://archive.ics.uci.edu/ml/index.php
Rezaee, J.-A., Jasni, J.: Parameter selection in particle swarm optimisation: a survey. J. Exp. Theor. Artif. Intell. 25(4), 527–542 (2013)
Lane, M.C., Xue, B., Liu, I., Zhang, M.: Gaussian based particle swarm optimisation and statistical clustering for feature selection. In: Blum, C., Ochoa, G. (eds.) EvoCOP 2014. LNCS, vol. 8600, pp. 133–144. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44320-0_12
Nguyen, H.B., Xue, B., Liu, I., Andreae, P., Zhang, M.: Gaussian transformation based representation in particle swarm optimisation for feature selection. In: Mora, A.M., Squillero, G. (eds.) EvoApplications 2015. LNCS, vol. 9028, pp. 541–553. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16549-3_44
Nguyen, H.B., Xue, B., Liu, I., Zhang, M.: PSO and statistical clustering for feature selection: a new representation. In: Dick, G., et al. (eds.) SEAL 2014. LNCS, vol. 8886, pp. 569–581. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13563-2_48
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Chen, K., Zhou, F., Xue, B. (2018). Particle Swarm Optimization for Feature Selection with Adaptive Mechanism and New Updating Strategy. In: Mitrovic, T., Xue, B., Li, X. (eds) AI 2018: Advances in Artificial Intelligence. AI 2018. Lecture Notes in Computer Science(), vol 11320. Springer, Cham. https://doi.org/10.1007/978-3-030-03991-2_39
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