Abstract
Due to the accelerated growth of unlabeled data, unsupervised classification methods have become of great importance, and clustering is one of the main approaches among these methods. However, the performance of any clustering algorithm is highly dependent on the quality of the features used for the task. This work presents a Differential Evolution algorithm for maximizing an unsupervised clustering measure. Results are evaluated using unsupervised clustering metrics, suggesting that the Differential Evolution algorithm can achieve higher scores when compared to other feature selection methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)
Gantz, J.F.: The diverse and exploding digital universe: an updated forecast of worldwide information growth through 2011. Technical report, IDC (2008)
Lloyd, S.P.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129–137 (1982)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Piscataway, NJ, vol. 1, pp. 886–893. IEEE Press (2005)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, (USA), vol. 1, pp. 1097–1105. Curran Associates Inc. (2012)
Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16–28 (2014)
Song, Q., Ni, J., Wang, G.: A fast clustering-based feature subset selection algorithm for high-dimensional data. IEEE Trans. Knowl. Data Eng. 25(1), 1–14 (2013)
Law, M.H., Figueiredo, M.A., Jain, A.K.: Simultaneous feature selection and clustering using mixture models. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1154–1166 (2004)
Li, Z., Liu, J., Yang, Y., Zhou, X., Lu, H.: Clustering-guided sparse structural learning for unsupervised feature selection. IEEE Trans. Knowl. Data Eng. 26(9), 2138–2150 (2014)
Tabakhi, S., Moradi, P., Akhlaghian, F.: An unsupervised feature selection algorithm based on ant colony optimization. Eng. Appl. Artif. Intell. 32(1), 112–123 (2014)
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)
Nag, K., Pal, N.R.: A multi-objective genetic programming-based ensemble for simultaneous feature selection and classification. IEEE Trans. Cybern. 46(2), 499–510 (2016)
Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)
Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)
Hattori, L.T., Lopes, H.S., Lopes, F.M.: Evolutionary computation and swarm intelligence for the inference of gene regulatory networks. Int. J. Innov. Comput. Appl. 7(4), 225–235 (2016)
Lin, C., Qing, A., Feng, Q.: A comparative study of crossover in differential evolution. J. Heuristics 17(6), 675–703 (2011)
Krause, J., Lopes, H.S.: A comparison of differential evolution algorithm with binary and continuous encoding for the MKP. In: Proceedings of the BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence, pp. 381–387 (2013)
Caliński, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat.-Theory Methods 3(1), 1–27 (1974)
Acknowledgements
Author M. Gutoski and L.T. Hattori would like to thank CAPES for the scholarship; Author M. Ribeiro would like to thank the Catarinense Federal Institute of Education, Science and Technology and IFC/CAPES/Prodoutoral for the scholarship; Author N. Aquino would like to thank the Organization of the American States, the Coimbra Group of Brazilian Universities and the Pan American Health Organization; author H. S. Lopes would like to thank to CNPq for the research grant number 440977/2015-0.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Gutoski, M., Ribeiro, M., Aquino, N.M.R., Hattori, L.T., Lazzaretti, A.E., Lopes, H.S. (2018). Feature Selection Using Differential Evolution for Unsupervised Image Clustering. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10841. Springer, Cham. https://doi.org/10.1007/978-3-319-91253-0_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-91253-0_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91252-3
Online ISBN: 978-3-319-91253-0
eBook Packages: Computer ScienceComputer Science (R0)