Abstract
Particle swarm optimization(PSO) has been applied on feature selection with improved results. Traditional PSO methods have some drawbacks when dealing with binary space, which may bring negative effects on the results. In this paper, an algorithm based on fitness proportionate selection binary particle swarm optimization(FPSBPSO) will be discussed in detail aiming to overcome the problems of traditional PSO methods. FPSBPSO will be utilized in the feature subset selection domain. The performance of feature selection will be compared in a benchmark dataset, and experimental results prove that the FPSBPSO-based feature selection methods can avoid premature convergence and improve the classification accuracy at the same time.
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References
Abbasi, A., Chen, H., Salem, A.: Sentiment analysis in multiple languages: Feature selection for opinion classification in web forums. ACM Transactions on Information Systems (TOIS) 26(3), 12 (2008)
Basu, T., Murthy, C.: Effective text classification by a supervised feature selection approach. In: 2012 IEEE 12th International Conference on Data Mining Workshops (ICDMW), pp. 918–925. IEEE (2012)
Beasley, J.E.: Or-library: distributing test problems by electronic mail. Journal of the Operational Research Society 41(11), 1069–1072 (1990)
Eberhart, R., Simpson, P., Dobbins, R.: Computational intelligence PC tools. Academic Press Professional, Inc., San Diego (1996)
Hassan, R., Cohanim, B., de Weck, O.: A comparison of particle swarm optimization and the genetic algorithm. In: Proceedings of the 1st AIAA Multidisciplinary Design Optimization Specialist Conference (April 2005)
Kennedy, J.: Bare bones particle swarms. In: Proceedings of IEEE Swarm Intelligence Symposium, pp. 80–87 (April 2003)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Kennedy, J., Eberhart, R.: A discrete binary version of the particle swarm optimization. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, Computational Cybernetics and Simulation, vol. 5, pp. 4104–4108 (October 1997)
Khuri, S., Back, T., Heitkotter, J.: The zero/one multiple knapsack problem and genetic algorithms. In: Proceedings of the 1994 ACM Symposium on Applied Computing, pp. 188–193 (April 1994)
Liao, C.-J., Tseng, C.-T., Luarn, P.: A discrete version of particle swarm optimization for flowshop scheduling problems. Computers & Operations Research 34(10), 3099–3111 (2007)
Liu, X., Shang, L.: A fast wrapper feature subset selection method based on binary particle swarm optimization. In: Proceedings of IEEE Congress on Evolutionary Computation, pp. 3347–3353 (June 2013)
Shi, X., Liang, Y., Lee, H., Lu, C., Wang, Q.: Particle swarm optimization-based algorithms for tsp and generalized tsp. Information Processing Letters 103(5), 169–176 (2007)
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proceedings of IEEE World Congress on Computational Intelligence, pp. 69–73 (May 1998)
Song, Q., Ni, J., Wang, G.: A fast clustering-based feature subset selection algorithm for high-dimensional data. IEEE Transactions on Knowledge and Data Engineering 25(1), 1–14 (2013)
Tasgetiren, M.F., Liang, Y.-C.: A binary particle swarm optimization algorithm for lot sizing problem. Journal of Economic and Social Research 5(2), 1–20 (2004)
Trelea, I.C.: The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf. Process. Lett. 85(6), 317–325 (2003)
Wang, X., Yang, J., Teng, X., Xia, W., Jensen, R.: Feature selection based on rough sets and particle swarm optimization. Pattern Recognition Letters 28(4), 459–471 (2007)
Xue, B., Zhang, M., Browne, W.N.: Novel initialisation and updating mechanisms in PSO for feature selection in classification. In: Esparcia-Alcázar, A.I. (ed.) EvoApplications 2013. LNCS, vol. 7835, pp. 428–438. Springer, Heidelberg (2013)
Xue, B., Zhang, M., Browne, W.N.: Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Transactions on Cybernetics 43(6), 1656–1671 (2013)
Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: ICML, vol. 97, pp. 412–420 (1997)
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Zhou, Z., Liu, X., Li, P., Shang, L. (2014). Feature Selection Method with Proportionate Fitness Based Binary Particle Swarm Optimization. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_49
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DOI: https://doi.org/10.1007/978-3-319-13563-2_49
Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-13563-2
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