Fuzzy quartile encoding as a preprocessing method for biomedical pattern classification

A fuzzy set based preprocessing method is described that may be used in the classification of patterns. This method, dispersion-adjusted fuzzy quartile encoding, determines the respective degrees to which a feature (attribute) belongs to a collection of fuzzy sets that overlap at the respective quartile boundaries of the feature. The fuzzy sets are adjusted to take into account the overall dispersion of values for a feature. The membership values are subsequently used in place of the original feature value. This transformation has a normalizing effect on the feature space and is robust to feature outliers. This preprocessing method, empirically evaluated using five biomedical datasets, is shown to improve the discriminatory power of the underlying classifiers.

[1]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[2]  D Fewer,et al.  Classification of 1H MR spectra of human brain neoplasms: The influence of preprocessing and computerized consensus diagnosis on classification accuracy , 1996, Journal of magnetic resonance imaging : JMRI.

[3]  David G. Stork,et al.  Pattern Classification , 1973 .

[4]  D. M. Titterington,et al.  Neural Networks: A Review from a Statistical Perspective , 1994 .

[5]  F. Kianifard.  Applied Multivariate Data Analysis: Volume II: Categorical and Multivariate Methods , 1994 .

[6]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[7]  Max A. Little,et al.  Suitability of Dysphonia Measurements for Telemonitoring of Parkinson's Disease , 2008, IEEE Transactions on Biomedical Engineering.

[8]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[9]  Andries Petrus Engelbrecht,et al.  Fundamentals of Computational Swarm Intelligence , 2005 .

[10]  P. J. Huber.  Robust Estimation of a Location Parameter , 1964 .

[11]  Metin Akay,et al.  Nonlinear Biomedical Signal Processing: Fuzzy Logic, Neural Networks, and New Algorithms , 2000 .

[12]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[13]  Andrew R. Webb,et al.  Statistical Pattern Recognition , 1999 .

[14]  Lior Rokach,et al.  Data Mining And Knowledge Discovery Handbook , 2005 .

[15]  Vojislav Kecman,et al.  Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models , 2001 .

[16]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[17]  W. Press,et al.  Numerical Recipes: The Art of Scientific Computing , 1987 .

[18]  D. L. Pavia,et al.  Introduction to Spectroscopy , 1978 .

[19]  Lipo Wang.  Support vector machines : theory and applications , 2005 .

[20]  Yuichiro Anzai,et al.  Pattern Recognition and Machine Learning , 1992, Springer US.

[21]  John von Neumann,et al.  Theory Of Self Reproducing Automata , 1967 .

[22]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .

[23]  Grzegorz Rozenberg,et al.  The many facets of natural computing , 2008, Commun. ACM.

[24]  Ray L. Somorjai,et al.  Neural network classification of infrared spectra of control and Alzheimer's diseased tissue , 1995, Artif. Intell. Medicine.

[25]  V. Vapnik.  Pattern recognition using generalized portrait method , 1963 .

[26]  Lukasz A. Kurgan,et al.  Knowledge discovery approach to automated cardiac SPECT diagnosis , 2001, Artif. Intell. Medicine.

[27]  Sankar K. Pal,et al.  Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing , 1999 .

[28]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[29]  Grzegorz Rozenberg,et al.  Computer Science, Informatics, and Natural Computing—Personal Reflections , 2008 .

[30]  Paul D. Gader,et al.  2009 Special Issue: RKF-PCA: Robust kernel fuzzy PCA , 2009 .

[31]  Witold Pedrycz,et al.  Variance analysis and biomedical pattern classification , 2010, International Conference on Fuzzy Systems.

[32]  Ramanathan Gnanadesikan,et al.  Methods for statistical data analysis of multivariate observations , 1977, A Wiley publication in applied statistics.

[33]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[34]  Sanghamitra Bandyopadhyay,et al.  Analysis of Biological Data: A Soft Computing Approach , 2007, Science, Engineering, and Biology Informatics.

[35]  László Györfi,et al.  A Probabilistic Theory of Pattern Recognition , 1996, Stochastic Modelling and Applied Probability.

[36]  Robert J. Schalkoff,et al.  Pattern recognition - statistical, structural and neural approaches , 1991 .

[37]  D. Dasgupta.  Artificial Immune Systems and Their Applications , 1998, Springer Berlin Heidelberg.

[38]  Friedrich Steimann,et al.  Fuzzy set theory in medicine , 1997, Artif. Intell. Medicine.

[39]  Lipo Wang,et al.  Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing) , 2005 .

[40]  William H. Press,et al.  Numerical Recipes 3rd Edition: The Art of Scientific Computing , 2007 .

[41]  Ian Witten,et al.  Data Mining , 2000 .

[42]  I. Bernstein.  Applied Multivariate Analysis , 1988 .

[43]  Brian D. Ripley,et al.  Pattern Recognition and Neural Networks , 1996 .

[44]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[45]  Nicolino J. Pizzi.  Bleeding predisposition assessments in tonsillectomy/adenoidectomy patients using fuzzy interquartile encoded neural networks , 2001, Artif. Intell. Medicine.

[46]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[47]  H. Friebolin,et al.  Basic one- and two-dimensional NMR spectroscopy , 1991 .

[48]  Andrea Sorbi,et al.  New Computational Paradigms: Changing Conceptions of What is Computable , 2007 .

[49]  Gheorghe Paun,et al.  Membrane Computing , 2002, Natural Computing Series.

[50]  Ivan Bratko,et al.  ASSISTANT 86: A Knowledge-Elicitation Tool for Sophisticated Users , 1987, EWSL.

[51]  Bernadette Bouchon-Meunier,et al.  Modern Information Processing: From Theory to Applications , 2011 .