Modified Binary Inertial Particle Swarm Optimization for Gene Selection in DNA Microarray Data
DNA microarrays are being used to characterize the genetic expression of several illnesses, such as cancer. There has been interest in developing automated methods to classify the data generated by those microarrays. The problem is complex due to the availability of just a few samples to train the classifiers, and the fact that each sample may contain several thousands of features. One possibility is to select a reduced set of features (genes). In this work we propose a wrapper method that is a modified version of the Inertial Geometric Particle Swarm Optimization.We name it MIGPSO. We compare MIGPSO with other approaches. The results are promising. MIGPSO obtained an increase in accuracy of about 4 %. The number of genes selected is also competitive.
KeywordsDNA microarray PSO Feature selection Wrapper method
The authors thank Tecnológico de Monterrey, Campus Guadalajara, as well as IPN-CIC under project SIP 20151187, and CONACYT under project 155014 for the economical support to carry out this research.
- 1.Koller, D., Sahami, M.: Toward optimal feature selection. Technical report, Stanford InfoLab, Stanford University (1996)Google Scholar
- 5.Xing, E.P., Jordan, M.I., Karp, R.M., et al.: Feature selection for high-dimensional genomic microarray data. In: ICML, vol. 1, pp. 601–608. Citeseer (2001)Google Scholar
- 8.Chiang, Y.M., Chiang, H.M., Lin, S.Y.: The application of ant colony optimization for gene selection in microarray-based cancer classification. Int. Conf. Mach. Learn. Cybern. 7, 4001–4006 (2008)Google Scholar
- 11.Saraswathi, S., Sundaram, S., Sundararajan, N., Zimmermann, M., Nilsen-Hamilton, M.: ICGA-PSO-ELM approach for accurate multiclass cancer classification resulting in reduced gene sets in which genes encoding secreted proteins are highly represented. T. Comput. Biol. Bioinf. 8(2), 452–463 (2011)Google Scholar
- 15.Kennedy, J., Eberhart, R.: Particle swarm optimization. IEEE Int. Conf. Neural Networks. 4, 1942–1948 (1995)Google Scholar
- 19.Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: IEEE Conference Evolutionary Computation., pp. 69–73 (1998)Google Scholar
- 20.Moraglio, A., Di Chio, C., Togelius, J., Poli, R.: Geometric particle swarm optimization. J. Artif. Evol. Appl. 11, 247–250 (2008)Google Scholar
- 21.Moraglio, A., Togelius, J.: Inertial geometric particle swarm optimization. In: IEEE Conference on Evolutionary Computation, pp. 1973–1980 (2009)Google Scholar
- 22.Alba, E., García-Nieto, J., Jourdan, L., Talbi, E.G.: Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms. In: IEEE Conference on Evolutionary Computation, pp. 284–290 (2007)Google Scholar