A multilevel tabu search algorithm for the feature selection problem in biomedical data

The automated analysis of patients' biomedical data can be used to derive diagnostic and prognostic inferences about the observed patients. Many noninvasive techniques for acquiring biomedical samples generate data that are characterized by a large number of distinct attributes (i.e., features) and a small number of observed patients (i.e., samples). Using these biomedical data to derive reliable inferences, such as classifying a given patient as either cancerous or noncancerous, requires that the ratio r of the number of samples to the number of features be within the range 5

[1]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[2]  Richard Baumgartner,et al.  Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions , 2003, Bioinform..

[3]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Teodor Gabriel Crainic,et al.  A first multilevel cooperative algorithm for capacitated multicommodity network design , 2006, Comput. Oper. Res..

[5]  Toby Walsh,et al.  An Empirical Analysis of Search in GSAT , 1993, J. Artif. Intell. Res..

[6]  A. N. Stevens,et al.  NMR Studies of metabolites in living tissue , 1982 .

[7]  Weiqi Li,et al.  Dynamics of Local Search Trajectory in Traveling Salesman Problem , 2005, J. Heuristics.

[8]  Thomas M. Cover,et al.  The Best Two Independent Measurements Are Not the Two Best , 1974, IEEE Trans. Syst. Man Cybern..

[9]  Fred W. Glover,et al.  Tabu Search , 1997, Handbook of Heuristics.

[10]  Cesare Furlanello,et al.  An accelerated procedure for recursive feature ranking on microarray data , 2003, Neural Networks.

[11]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[12]  Larry A. Rendell,et al.  The Feature Selection Problem: Traditional Methods and a New Algorithm , 1992, AAAI.

[13]  Keinosuke Fukunaga,et al.  A Branch and Bound Algorithm for Feature Subset Selection , 1977, IEEE Transactions on Computers.

[14]  Geoffrey J McLachlan,et al.  Selection bias in gene extraction on the basis of microarray gene-expression data , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Robert P. W. Duin,et al.  STATISTICAL PATTERN RECOGNITION , 2005 .

[16]  Chi Hau Chen,et al.  Pattern recognition and signal processing , 1978 .

[17]  R L Somorjai,et al.  Near‐optimal region selection for feature space reduction: novel preprocessing methods for classifying MR spectra , 1998, NMR in biomedicine.

[18]  Shashi Shekhar,et al.  Multilevel hypergraph partitioning: application in VLSI domain , 1997, DAC.

[19]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

[20]  Shashi Shekhar,et al.  Multilevel hypergraph partitioning: applications in VLSI domain , 1999, IEEE Trans. Very Large Scale Integr. Syst..

[21]  Stephen A Bustin,et al.  The value of microarray techniques for quantitative gene profiling in molecular diagnostics. , 2002, Trends in molecular medicine.

[22]  Jack Sklansky,et al.  A note on genetic algorithms for large-scale feature selection , 1989, Pattern Recognit. Lett..

[23]  Josef Kittler,et al.  Fast branch & bound algorithms for optimal feature selection , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  J. Kittler,et al.  Feature Set Search Alborithms , 1978 .

[25]  David W. Aha,et al.  A Comparative Evaluation of Sequential Feature Selection Algorithms , 1995, AISTATS.

[26]  Chris Walshaw,et al.  Multilevel Refinement for Combinatorial Optimisation Problems , 2004, Ann. Oper. Res..

[27]  Hongbin Zhang,et al.  Feature selection using tabu search method , 2002, Pattern Recognit..