Abstract
In this paper we propose a flexible method for analyzing the relevance of input variables in high dimensional problems with respect to a given dichotomic classification problem. Both linear and non-linear cases are considered. In the linear case, the application of derivative-based saliency yields a commonly adopted ranking criterion. In the non-linear case, the method is extended by introducing a resampling technique and by clustering the obtained results for stability of the estimate. The method was preliminarly validated on the data published by T.R. Golub et al. on a study, at the molecular level, of two kinds of leukemia: Acute Myeloid Leukemia and Acute Lymphoblastic Leukemia (Science 5439-286, 531-537, 1999). Our technique indicates that, among the top 20 genes found by the final cluster analysis, 8 of the 50 genes listed in the original work feature a stronger discriminating power.
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References
Aurenhammer, F.: Voronoi diagrams-a survey of a fundamental geometric data structure. ACM Computing Surveys 3(23), 345–405 (1991)
Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Plenum, NewYork (1981)
Bilban, M., Buehler, L.K., Head, S., Desoye, G., Quaranta, V.: Normalizing DNA microarray data. Curr. Issues Mol. Biol. 4(2), 57–64 (2002)
Brank, J., Grobelnik, M., Milic-Frayling, N., Mladenic, D.: Feature selection using linear support vector machines, Tech. Rep. MSR-TR-2002-63, Microsoft Research (June 2002)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge Univ. Press, Cambridge (2000)
Dietterich, T.G.: Machine-learning research: Four current directions. The AI Magazine 4(18), 97–136 (1998)
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. John Wiley and Sons, New York (1973)
Golub, T.R., et al.: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 5439(286), 531–537 (1999)
Ho, T.K., Basu, M.: Complexity measures of supervised classification problems. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(3), 289–300 (2002)
Krishnapuram, R., Keller, J.M.: A possibilistic approach to clustering. IEEE Trans. on Fuzzy Systems 2(1), 98–110 (1993)
Krishnapuram, R., Keller, J.M.: The possibilistic c-Means algorithm: insights and recommendations. IEEE Trans. on Fuzzy Systems 3(4), 385–393 (1996)
Masulli, F., Rovetta, S.: Soft transition from probabilistic to possibilistic fuzzy clustering, DISI Technical Report DISI-TR-03-02, Department of Computer and Information Sciences, University of Genoa, Italy (April 2002), http://www.disi.unige.it/person/RovettaS/research/techrep/DISI-TR-02-03.ps.gz
Moneta, C., Parodi, G., Rovetta, S., Zunino, R.: Automated diagnosis and disease characterization using neural network analysis. In: Proc. of the 1992 IEEE Int. Conf. on Systems, Man and Cybernetics, Chicago USA, October 1992, pp. 123–128 (1992)
Ripley, B.D.: Pattern recognition and neural networks. Cambridge Univ. Press, Cambridge (1996)
Rose, K.: Deterministic annealing for clustering, compression, classification, regression, and related optimization problems. Proceedings of IEEE 11(86), 2210–2239 (1998)
Sindhwani, V., Bhattacharya, P., Rakshit, S.: Information theoretic feature crediting in multiclass support vector machines. In: 1st SIAM Int. Conf. on Data Mining, Chicago, USA, SIAM, Philadelphia (2001)
Weller, F.: Stability ofVoronoi neighborhood under perturbations of the sites. In: Proc. of Ninth Canadian Conf. on Computational Geometry, Kingston, Ontario, Canada (August 1997)
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Masulli, F., Rovetta, S. (2003). Gene Selection Using Random Voronoi Ensembles. In: Apolloni, B., Marinaro, M., Tagliaferri, R. (eds) Neural Nets. WIRN 2003. Lecture Notes in Computer Science, vol 2859. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45216-4_34
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DOI: https://doi.org/10.1007/978-3-540-45216-4_34
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