Commenced in January 2007
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Dataset Analysis Using Membership-Deviation Graph
Abstract:Classification is one of the primary themes in computational biology. The accuracy of classification strongly depends on quality of a dataset, and we need some method to evaluate this quality. In this paper, we propose a new graphical analysis method using 'Membership-Deviation Graph (MDG)' for analyzing quality of a dataset. MDG represents degree of membership and deviations for instances of a class in the dataset. The result of MDG analysis is used for understanding specific feature and for selecting best feature for classification.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1059453Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1048
 H. Liu, J. Li, L. Wong, "A Comparative Study on Feature Selection and Classification Methods Using Gene Expression Profiles and Proteomic Patterns", Gene Informatics 13, 2002, pp51-60.
 S. Doraisamy, S. Golzari, N.M. Norowi, M.N.B Sulaiman, N.I. Udzir, "A Study on Feature selection and Classification Techniques for Automatic Genre Classification of Traditional Malay Music", Proc. of International Conference on Music Information Retrieval, 2008, pp331- 336.
 I. Guyon, A. Elisseeff, "An introduction to variable and feature selection", J. Mach. Learn. Res. 3, 2003, pp.1157-1182.
 R. Gilad-Bachrac, A. Navot, N. Tishby, "Margin based feature selection"theory and algorithms", Proceedings of the 21st International Conference on Machine Learning, 2004.
 K.H. Quah, C. Quek, "MCES: a novel Monte Carlo evaluative selection approach for objective feature selections", IEEE Trans. Neural Networks 18 (2), 2007.
 J. Dy, C.E. Brodley, "Feature selection for unsupervised learning", J. Mach. Learn. Res. 5, 2005, pp845-889 2005.
 K. Kira, L.A. Rendell, "A Practical Approach to Feature Selection", Proceedings of the Ninth International Conference on Machine Learning, 1992, pp249-256.
 W.S. Meisel, Computer-Oriented Approaches to Pattern Recognition, Academic Press, New York, 1972.
 S. Piramuthu, "The Housdorff Distance Measure for Feature Selection in Learning Applications", Proceedings of the 32nd Hawaii International Conference on System Sciencespp1-6, 1999.
 J. Liang, S. Yang, A. Winstanley, "Invariant Optimal Feature Selection: A Distance Discriminant and Feature Rranking Based Solution", The journal of the pattern recognition, 2008, pp1429-1439.
 K. Kira, and L.A. Rendell, "The feature selection problem: Traditional methods and a new algorithm", Proceedings of Ninth National Conference on Artificial Intelligence, 1992, pp129-134.
 Y. Sun and D. Wu, "A RELIEF Based Feature Extraction Algorithm", Proceedings of the 2008 SIAM International Conference on Data Mining, 2008, pp188-195.
 I. Kononenko, E. Simec, M. Robnik-Sikonja, "Overcoming the myopia of induction learning algorithms with RELIEFF", Applied Intelligence Vol7, 1, 1997, pp.39-55
 K. Nakai, Yeast Dataset, http://archive.ics.uci.edu/ml/datasets/Yeast.