Abstract
The problem of feature selection has been paramount in the last years, since it can be as important as the classification step itself. The main goal of feature selection is to find out the subset of features that optimize some fitness function, often in terms of a classifier’s accuracy or even the computational burden for extracting each feature. Therefore, the approaches to feature selection can be modeled as optimization tasks. In this chapter, we evaluate a binary-constrained version of the Flower Pollination Algorithm (FPA) for feature selection, in which the search space is a boolean lattice where each possible solution, or a string of bits, denotes whether a feature will be used to compose the final set. Numerical experiments over some public and private datasets have been carried out and comparison with Particle Swarm Optimization, Harmony Search and Firefly Algorithm has demonstrated the suitability of the FPA for feature selection.
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Notes
- 1.
The first four datasets can be found on http://featureselection.asu.edu/datasets.php.
References
Bababdani, B.M., Mousavi, M.: Gravitational search algorithm: a new feature selection method for {QSAR} study of anticancer potency of imidazo[4,5-b]pyridine derivatives. Chemometr. Intell. Lab. Syst. 122(15), 1–11 (2013)
Chen, B., Chen, L., Chen, Y.: Efficient ant colony optimization for image feature selection. Signal Process. 93(6), 1566–1576 (2013). (special issue on Machine Learning in Intelligent Image Processing)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification, 2nd edn. Wiley, New York (2001)
Falcon, R., Almeida, M., Nayak, A.: Fault identification with binary adaptive fireflies in parallel and distributed systems. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1359–1366 (2011)
Firpi, HA., Goodman, E.: Swarmed feature selection. In: Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop. IEEE Computer Society, Washington, DC, pp. 112–118 (2004)
Geem, Z.W.: Music-inspired harmony search algorithm: theory and applications, 1st edn. Springer, Berlin (2009)
Kabir, M., Shahjahan, M., Murase, K.: An efficient feature selection using ant colony optimization algorithm. In: Leung, C., Lee, M., Chan, J. (eds.) Neural Information Processing, Lecture Notes in Computer Science, vol. 5864, pp. 242–252, Springer, Berlin (2009)
Kennedy, J., Eberhart, R.C.: Swarm intelligence. Morgan Kaufman, Burlington (2001)
Marinakis, Y., Marinaki, M., Matsatsinis, N.: A hybrid discrete artificial bee colony—GRASP algorithm for clustering. In: Proceedings of the International Conference on Computers Industrial Engineering, pp. 548–553 (2009)
Oh, I.S., Lee, J.S., Moon, B.R.: Hybrid genetic algorithms for feature selection. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1424–1437 (2004)
Palit, S., Sinha, S.N., Molla, M.A., Khanra, A., Kule, M.: A cryptanalytic attack on the knapsack cryptosystem using binary firefly algorithm. In: 2nd International Conference on Computer and Communication Technology (ICCCT), pp. 428–432 (2011)
Papa, J.P., Falcão, A.X., Suzuki, C.T.N.: Supervised pattern classification based on optimum-path forest. Int. J. Imaging Syst. Technol. 19(2), 120–131 (2009)
Papa, J.P., Falcão, A.X., Albuquerque, V.H.C., Tavares, J.M.R.S.: Efficient supervised optimum-path forest classification for large datasets. Pattern Recogn. 45(1), 512–520 (2012)
Ramos, C.C.O., Souza, A.N., Chiachia, G., Falcão, A.X., Papa, J.P.: A novel algorithm for feature selection using harmony search and its application for non-technical losses detection. Comput. Electr. Eng. 37(6), 886–894 (2011)
Ramos, C.C.O., De Souza, A., Falcão, A., Papa, J.: New insights on nontechnical losses characterization through evolutionary-based feature selection. Power Deliv. IEEE Trans. 27(1), 140–146 (2012)
Ramos, C.C.O., de Souza, A.N., Falcão, A.X., Papa, J.P.: New insights on non-technical losses characterization through evolutionary-based feature selection. IEEE Trans. Power Deliv. 27(1), 140–146 (2012)
Rodrigues, D., Pereira, L.A.M., Almeida, T.N.S., Papa, J.P., Souza, A.N., Ramos, C.C.O., Yang, X.S.: A binary cuckoo search algorithm for feature selection. In: Proceedings of IEEE International Symposium on Circuits and Systems, pp. 465–468 (2013a)
Rodrigues, D., Pereira, L.A.M., Nakamura, R.Y.M., Costa, K.A.P., Yang, X.S., Souza, A.N., Papa, J.P.: A wrapper approach for feature selection based on bat algorithm and optimum-path forest. Expert Syst. Appl. 41(5), 2250–2258 (2013)
Rodrigues, D., Pereira, L.A.M., Papa, J.P., Ramos, C.C.O., Souza, A.N., Papa, L.P.: Optimizing feature selection through binary charged system search. In: Proceedings of 15th International Conference on Computer Analysis of Images and Patterns, pp. 377–384 (2013c)
Schiezaro, M., Pedrini, H.: Data feature selection based on artificial bee colony algorithm. EURASIP J. Image Video Process. 1, 1–8 (2013)
Sivagaminathan, R.K., Ramakrishnan, S.: A hybrid approach for feature subset selection using neural networks and ant colony optimization. Expert Syst. Appl. 33(1), 49–60 (2007)
Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics Bull. 1(6), 80–83 (1945)
Yang, J., Honavar, V.: Feature subset selection using a genetic algorithm. IEEE Intell. Syst. Appl. 13(2), 44–49 (1998)
Yang, X.S.: Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bio-Inspired Comput. 2(2), 78–84 (2010)
Yang, X.S.: Flower pollination algorithm for global optimization. In: Proceedings of the 11th International Conference on Unconventional Computation and Natural Computation, pp. 240–249. Springer, Berlin (2012)
Yang, X.S., Karamanoglu, M., He, X.: Flower pollination algorithm: a novel approach for multiobjective optimization. Eng. Optim. 46(9), 1222–1237 (2014)
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Rodrigues, D., Yang, XS., de Souza, A.N., Papa, J.P. (2015). Binary Flower Pollination Algorithm and Its Application to Feature Selection. In: Yang, XS. (eds) Recent Advances in Swarm Intelligence and Evolutionary Computation. Studies in Computational Intelligence, vol 585. Springer, Cham. https://doi.org/10.1007/978-3-319-13826-8_5
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