Abstract
Feature selection is often used before a data mining or a machine learning task in order to build more accurate models. It is considered as a hard optimization problem and metaheuristics give very satisfactory results for such problems. In this work, we propose a hybrid metaheuristic that integrates a reinforcement learning algorithm with Bee Swarm Optimization metaheuristic (BSO) for solving feature selection problem. QBSO-FS follows the wrapper approach. It uses a hybrid version of BSO with Q-learning for generating feature subsets and a classifier to evaluate them. The goal of using Q-learning is to benefit from the advantage of reinforcement learning to make the search process more adaptive and more efficient. The performances of QBSO-FS are evaluated on 20 well-known datasets and the results are compared with those of original BSO and other recently published methods. The results show that QBO-FS outperforms BSO-FS for large instances and gives very satisfactory results compared to recently published algorithms.
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References
Battiti, R., Brunato, M.: Reactive search optimization: learning while optimizing. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics, pp. 543–571. Springer, Boston (2010). https://doi.org/10.1007/978-1-4419-1665-5_18
Belkebir, R., Guessoum, A.: A hybrid BSO-CHI2-SVM approach to Arabic text categorization. In: 2013 ACS International Conference on Computer Systems and Applications (AICCSA), pp. 1–7. IEEE (2013)
Blum, A.L., Langley, P.: Selection of relevant features and examples in machine learning. Artif. Intell. 97(1–2), 245–271 (1997)
Calvet, L., de Armas, J., Masip, D., Juan, A.A.: Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs. Open Math. 15(1), 261–280 (2017)
Chuang, L.Y., Tsai, S.W., Yang, C.H.: Improved binary particle swarm optimization using catfish effect for feature selection. Expert Syst. Appl. 38(10), 12699–12707 (2011)
Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1(1–4), 131–156 (1997)
Djeffal, M., Drias, H.: Multilevel bee swarm optimization for large satisfiability problem instances. In: Yin, H., Tang, K., Gao, Y., Klawonn, F., Lee, M., Weise, T., Li, B., Yao, X. (eds.) IDEAL 2013. LNCS, vol. 8206, pp. 594–602. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41278-3_72
Djenouri, Y., Drias, H., Chemchem, A.: A hybrid bees swarm optimization and tabu search algorithm for association rule mining. In: 2013 World Congress on Nature and Biologically Inspired Computing, pp. 120–125. IEEE (2013)
Drias, H., Mosteghanemi, H.: Bees swarm optimization based approach for web information retrieval. In: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 1, pp. 6–13. IEEE (2010)
Drias, H., Sadeg, S., Yahi, S.: Cooperative bees swarm for solving the maximum weighted satisfiability problem. In: Cabestany, J., Prieto, A., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 318–325. Springer, Heidelberg (2005). https://doi.org/10.1007/11494669_39
Huang, J., Cai, Y., Xu, X.: A hybrid genetic algorithm for feature selection wrapper based on mutual information. Pattern Recogn. Lett. 28(13), 1825–1844 (2007)
Kabir, M.M., Shahjahan, M., Murase, K.: A new hybrid ant colony optimization algorithm for feature selection. Expert Syst. Appl. 39(3), 3747–3763 (2012)
Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237–285 (1996)
Ke, L., Feng, Z., Ren, Z.: An efficient ant colony optimization approach to attribute reduction in rough set theory. Pattern Recogn. Lett. 29(9), 1351–1357 (2008)
Liu, H., Motoda, H.: Less is more. In: Computational Methods of Feature Selection, pp. 16–31. Chapman and Hall/CRC (2007)
Liu, H., Yu, L.: Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowl. Data Eng. 4, 491–502 (2005)
Oliveira, L.S., Sabourin, R., Bortolozzi, F., Suen, C.Y.: A methodology for feature selection using multiobjective genetic algorithms for handwritten digit string recognition. Int. J. Pattern Recogn. Artif. Intell. 17(06), 903–929 (2003)
Rostami, M., Moradi, P.: A clustering based genetic algorithm for feature selection. In: 2014 6th Conference on Information and Knowledge Technology (IKT), pp. 112–116. IEEE (2014)
Sadeg, S., Drias, H.: A selective approach to parallelise bees swarm optimisation metaheuristic: application to max-w-sat. Int. J. Innovative Comput. Appl. 1(2), 146–158 (2007)
Sadeg, S., Hamdad, L., Benatchba, K., Habbas, Z.: BSO-FS: bee swarm optimization for feature selection in classification. In: Rojas, I., Joya, G., Catala, A. (eds.) IWANN 2015. LNCS, vol. 9094, pp. 387–399. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19258-1_33
Sutton, R.S., Barto, A.G., et al.: Introduction to Reinforcement Learning, vol. 135. MIT press, Cambridge (1998)
Talbi, E.G.: Combining metaheuristics with mathematical programming, constraint programming and machine learning. Ann. Oper. Res. 240(1), 171–215 (2016)
Tawhid, M.A., Dsouza, K.B.: Hybrid binary bat enhanced particle swarm optimization algorithm for solving feature selection problems. Appl. Comput. Inf. (2018)
Wauters, T., Verbeeck, K., De Causmaecker, P., Berghe, G.V.: Boosting metaheuristic search using reinforcement learning. In: Talbi, E.G. (ed.) Hybrid Metaheuristics. SCI, pp. 433–452. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-30671-6_17
Xue, B., Zhang, M., Browne, W.N.: Particle swarm optimisation for feature selection in classification: novel initialisation and updating mechanisms. Appl. Soft Comput. 18, 261–276 (2014)
Yang, J., Honavar, V.: Feature subset selection using a genetic algorithm. In: Liu, H., Motoda, H. (eds.) Feature Extraction, Construction and Selection, pp. 117–136. Springer, Boston (1998). https://doi.org/10.1007/978-1-4615-5725-8_8
Zawbaa, H.M., Emary, E., Parv, B.: Feature selection based on antlion optimization algorithm. In: 2015 Third World Conference on Complex Systems (WCCS), pp. 1–7. IEEE (2015)
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Sadeg, S., Hamdad, L., Remache, A.R., Karech, M.N., Benatchba, K., Habbas, Z. (2019). QBSO-FS: A Reinforcement Learning Based Bee Swarm Optimization Metaheuristic for Feature Selection. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11507. Springer, Cham. https://doi.org/10.1007/978-3-030-20518-8_65
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