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Time-efficient estimation of conditional mutual information for variable selection in classification

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  • Todorov, Diman
  • Setchi, Rossi

Abstract

An algorithm is proposed for calculating correlation measures based on entropy. The proposed algorithm allows exhaustive exploration of variable subsets on real data. Its time efficiency is demonstrated by comparison against three other variable selection methods based on entropy using 8 data sets from various domains as well as simulated data. The method is applicable to discrete data with a limited number of values making it suitable for medical diagnostic support, DNA sequence analysis, psychometry and other domains.

Suggested Citation

  • Todorov, Diman & Setchi, Rossi, 2014. "Time-efficient estimation of conditional mutual information for variable selection in classification," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 105-127.
    • Handle: RePEc:eee:csdana:v:72:y:2014:i:c:p:105-127
    DOI: 10.1016/j.csda.2013.10.026
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    1. Maria Iannario, 2010. "On the identifiability of a mixture model for ordinal data," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 87-94.
    2. de Leeuw, Jan, 2006. "Principal component analysis of binary data by iterated singular value decomposition," Computational Statistics & Data Analysis, Elsevier, vol. 50(1), pages 21-39, January.
    3. Álvarez-Esteban, P.C. & del Barrio, E. & Cuesta-Albertos, J.A. & Matrán, C., 2013. "Searching for a common pooling pattern among several samples," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 1-14.
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