Abstract
This chapter focuses on a few key applications of hybrid intelligence techniques in the field of feature selection and classification. Hybrid intelligent techniques have been used to develop an effective and generalized learning model for solving these applications. The first application employs a new evolutionary hybrid feature selection technique for microarray datasets, which is implemented in two stages by integrating correlation-based binary particle swarm optimization (BPSO) with rough set algorithm to identify non-redundant genes capable of discerning between all objects. The other applications discussed are for evaluating the relative performance of different supervised classification procedures using hybrid feature reduction techniques. Correlation based Partial Least square hybrid feature selection method is used for feature extraction and the experimental results show that Partial Least Squares (PLS) regression method is an appropriate feature selection method and a combined use of different classification and feature selection approaches make it possible to construct high performance classification models for microarray data. Another hybrid algorithm, Correlation based reduct algorithm (CFS-RST) is used as a filter to eliminate redundant attributes and minimal reduct set is produced by rough sets. This method improves the efficiency and decreases the complexity of the classical algorithm. Extensive experiments are conducted on two public multi-class gene expression datasets and the experimental results show that hybrid intelligent methods are highly effective for selecting discriminative genes for improving the classification accuracy. The experimental results of all the applications indicate that, all the hybrid intelligent techniques discussed here have shown significant improvements in most of the binary and multi-class microarray datasets.
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References
Saeys, Y., Lnza, I., Larrañaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)
Somorjai, R.L., Dolenko, B., Baumgartner, R., Crow, J.E., Moore, J.H.: Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions. Bioinformatics 19, 1484–1491 (2003)
Wang, Y., Makedon, F., Ford, J., Pearlman, J.: Hykgene: a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data. Bioinformatics 21, 1530–1537 (2005)
Jafari, P., Azuaje, F.: An assessment of recently published gene expression data analyses: reporting experimental design and statistical factors. BMC Med Inform Decis Mak. 6(27) (2006)
Robnik-Šikonja, M., Kononenko, I.: Theoretical and empirical analysis of relieff and rrelieff. Machine Learning 53, 23–69 (2003)
Su, Y., Murali, T., Pavlovic, V., Schaffer, M., Kasif, S.: Rankgene: identification of diagnostic genes based on expression data. Bioinformatics 19, 1578–1579 (2003)
Kohavi, R., John, G.: Wrapper for feature subset selection. Artificial Intelligence 97, 273–324 (1997)
Blum, A., Langley, P.: Selection of relevant features and examples in machine learning. Artificial Intelligence 97, 245–271 (1997)
Li, L., Weinberg, C., Darden, T., Pedersen, L.: Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics 17, 1131–1142 (2001)
Ooi, C., Tan, P.: Genetic algorithms applied to multi-class prediction for the analysis of gene expression data. Bioinformatics 19, 37–44 (2003)
Jirapech-Umpai, T., Aitken, S.: Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes. BMC Bioinformatics 6(146) (2005)
Liu, J., Cutler, G., Li, W., Pan, Z., Peng, S., Hoey, T., Chen, L., Ling, X.: Multiclass cancer classification and biomarker discovery using GA-based algorithm. Bioinformatics 21, 2691–2697 (2005)
Kudo, M., Sklansky, J.: Comparison of algorithms that select features for pattern classifiers. Pattern Recognition 33, 25–41 (2000)
Kennedy, J., Eberhart, R.C.: Prticle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, Perth, Australia, vol. 4, pp. 1942–1948 (1995)
Kennedy, J., Eberhart, R.: A discrete binary version of the particle swarm algorithm. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, Orlando, FL, USA, vol. 5, pp. 4104–4108 (1997)
Juan, C.: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Transactions on Systems, Man and Cybernetics 34, 997–1006 (2004)
Deng, X.: Research on building crowd evacuation model based on multi-agent particle swarm optimization algorithm. Journal of Convergence Information Technology 8(4), 17–25 (2013)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (2004)
Quinlan, J.R.: Programs for machine learning. Morgan Kaufmann, CA (1993)
Yang, Y.H., Xiao, Y., Segal, M.R.: Identifying differentially expressed genes from microarray experiments via statistic synthesis. Bioinformatics 21(7), 1084–1093 (2005)
Pawlak, Z.: Rough set approach to knowledge-based decision support. European Journal of Operational Research 99, 48–57 (1997)
Mitra, S., Hayashi, Y.: Bioinformatics with Soft Computing. IEEE Transactions on Systems, Man and Cybernetics 36(5), 616–635 (2006)
Grzymala-Busse, J.W.: LERS-a system for learning from examples based on rough sets. In: Slowinski, R. (ed.) Intelligent Decision Support, pp. 3–18. Kluwer Academic Publishers, Dordrecht (1992)
Dash, S., Patra, B.: Redundant gene selection based on genetic and quick-reduct algorithm. International Journal on Data Mining and Intelligent Information Technology Applications 3(2) (2013)
Dash, S., Patra, B., Ttripathy, B.K.: A hybrid data mining technique for improving the classification accuracy of microarray data set. International Journal of Information Engineering and Electronic Business 2, 43–50 (2012)
Dash, S., Patra, B.: Rough set aided gene selection for cancer classification. In: Proceedings of 7th International Conference on Computer Sciences and Convergence Information Technology. IEEE Xplore, Seoul (2012)
Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11(5), 341–356 (1982)
Pawlak, Z.: Rough sets: Theoretical aspects of reasoning about data. Kluwer Academic Publishing, Dordrecht (1991)
Pawlak, Z.: Rough set approach to knowledge-based decision support. European Journal of Operational Research 99, 48–57 (1997)
Swiniarski, R.W., Skowron, A.: Rough set methods in feature selection and recognition. Pattern Recognition Letters 24(6), 833–849 (2003)
Vafaie, H., Imam, I.F.: Feature selection methods: genetic algorithms vs. greedy-like search. In: Proceedings of International Conference on Fuzzy and Intelligent Control Systems (1994)
Kennedy, J., Spears, W.M.: Matching algorithms to problems: An experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 39–43 (1998)
Juan, C.: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Transactions on Systems, Man and Cybernetics 34, 997–1006 (2004)
Jensen, R., Shen, Q.: Semantics-preserving dimensionality reduction: rough and fuzzy-rough based approaches. IEEE Transactions on Knowledge and Data Engineering 16 (12), 1457–1471 (2004)
Hall, M.A.: Correlation-based feature selection for machine learning. Ph.D. Thesis. Department of Computer Science, University of Waikato (1999)
Jensen, R.: Combining rough and fuzzy sets for feature selection. Ph.D. Dissertation. School of Informatics, University of Edinburgh (2004)
Ding, H., Peng, C.: Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computational Biology 3(2), 185–205 (2003)
Wold, H.: Soft modeling: the basic design and some extensions. Systems Under Indirect Observation 2, 1–53 (1982)
Wold, H.: Partial least squares. Encyclopedia of the Statistical Sciences 6, 581–591 (1985)
Wold, S., Ruhe, H., Wold, H., Dunn, W.J.: The collinearity problem in linear regression-The partial least squares (PLS) approach to generalized inverse. SIAM Journal of Scientific and Statistical Computations 5, 735–743 (1984)
Tan, A.C., Gilbert, D.: Ensemble machine learning on gene expression data for cancer classification. Applied Bioinformatics 2, 575–583 (2003)
Huang, X., Pan, W., Han, X., Chen, Y., Miller, L.W.: Borrowing information from relevant microarray studies for sample classification using weighted partial least squares. Comput. Biol. Chem. 29, 204–211 (2005)
Cao, K.A., Roussouw, D., Robert-Granie, C., Besse, P.: A Sparse PLS for variable selection when integrating omics data. Statistical Applications in Genetics and Molecular Biology 7 (2008)
Ding, B., Gentleman, R.: Classification using generalized partial least squares. Bioconductor Project (2004)
Fort, G., Lambert-Lacroix, S.: Classification using partial least squares with penalized logistic regression. Bioinformatics 21, 1104–1111 (2005)
Nguyen, D.V., Rocke, D.M.: Tumor classification by partial least squares using microarray gene expression data. Bioinformatics 18, 39–50 (2002)
Wold, H.: Soft modeling: the basic design and some extensions. Systems Under Indirect Observation 2, 1–53 (1982)
De Jong, S.: SIMPLS: an alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems 2(4), 251–263 (1993)
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Dash, S. (2015). Learning Using Hybrid Intelligence Techniques. In: Acharjya, D., Dehuri, S., Sanyal, S. (eds) Computational Intelligence for Big Data Analysis. Adaptation, Learning, and Optimization, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-319-16598-1_3
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DOI: https://doi.org/10.1007/978-3-319-16598-1_3
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