Feature selection based on a new formulation of the minimal-redundancy- maximal-relevance criterion

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4477 LNCS, Issue PART 1, 2007, Pages 47-54

  Ponsa, Daniel ^a   López, Antonio ^a  

^a UNIVERSITAT AUTÒNOMA DE BARCELONA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

DATA STRUCTURES; IMAGE CLASSIFICATION; INFORMATION THEORY;

DISCRETE-VALUED DATASETS; MINIMAL REDUNDANCY MAXIMAL RELEVANCE CRITERION;

FEATURE EXTRACTION;

EID: 38149029145     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-72847-4_8     Document Type: Conference Paper

References (7)

1. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research (3), 1157-1182 (2003)
2. Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97(1-2), 273-324 (1997)
3. Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8), 1226-1238 (2005)
4. Press, W.H., Flannery, B., Teukolsky, S.A., Vetterling, W.T.: Numerical recipes in c. Cambridge (1988)
5. Newman, D. J., Hettich, S., Blake, C., Merz, C.: UCI repository of machine learning databases (1998)
6. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm. (2001)
7. Dietterich, T.G.: Approximate statistical test for comparing supervised classification learning algorithms. Neural Computation 10(7), 1895-1923 (1998)