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Feature selection problem appears where large number of features constraint effective data analysis and processing. Identification of the most important feature subsets is a crucial challenge in many important applications. For example, a basic question in bioinformatics which is identification of genes functionalities, can be formulated and answered as a problem of this kind. Identification of the most important feature subsets through minimisation of convex and piecewise-linear (CPL) criterion function is described and analysed in the paper. This approach is combined with relaxation of the linear separability assumption.
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  • Politechnika Białostocka, Wydział Informatyki
Bibliografia
  • 1. Duda O. R., Hart P. E., Stork D. G.: Pattern Classification, J. Wiley, New York 2001.
  • 2. Liu H., Motoda H. (Eds.): Computational Methods of Feature Selection, Chapmann & Hall/ CRC, New York 2008.
  • 3. Fukunaga K.: Introduction to Stalistical Pattern Recognition, Academic Press 1972.
  • 4. Guyon I., Weston J., Barnhill S., Vapnik V. N.: Gene Selection for Cancer Classification using Support Vector Machines, Machine Learning, 2002, 46, 389-422.
  • 5. Bobrowski L., Łukaszuk T.: Selection of the linearly separable feature subsets, in: Artificial Intelligence and Soft Computing - ICAISC 2004, L. Rutkowski et al. (Eds.), Springer Lecture Notes in Artificial Intelligence 3070, Springer Verlag 2004, 544-549.
  • 6. Bobrowski L.: Feature subsets selection based on linear separability, in: VII-th ICB Seminar: Statistics and Clinical Practice, H. Bacelar-Nicolau, L. Bobrowski, J. Doroszewski, C. Kulikowski, N. Victor /Eds/, June 2008, Warsaw 17-23.
  • 7. Bobrowski L.: Data mining based on convex and piecewise linear (CPL) criterion functions (in Polish), Technical University Białystok 2005.
  • 8. Bobrowski L: Design of piecewise linear classifiers from formal neurons by some basis exchange technique. Pattern Recognition, 1991, 24, 9, 863-870.
  • 9. Vapnik V.N.: Statistical Learning Theory, J. Wiley, New York 1998.
  • 10. Asuncion A., Newman D.J.: UCI Machine Learning Repository [http://www.ics.uci.edu/~mlearn/ MLRepository.html], Irvine, CA: University of California, School of Information and Computer Science, 2007.
  • 11. Alon U., Barkai N., Notterman D.A., Gish K., Ybarra S., Mack D., Levine A. J.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays, Proc. Natl. Acad. Sci. USA, 1999, 96, Issue 12, June 8, 6745-6750.
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Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPZ3-0030-0021
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