Feature selection method based on fuzzy entropy for regression in QSAR studies

Zahra Elmi, Karim Faez, Mohammad Goodarzi, Nasser Goudarzi

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Feature selection and feature extraction are the most important steps in classification and regression systems. Feature selection is commonly used to reduce the dimensionality of datasets with tens or hundreds of thousands of features, which would be impossible to process further. Recent example includes quantitative structure-activity relationships (QSAR) dataset including 1226 features. A major problem of QSAR is the high dimensionality of the feature space; therefore, feature selection is the most important step in this study. This paper presents a novel feature selection algorithm that is based on entropy. The performance of the proposed algorithm is compared with that of a genetic algorithm method and a stepwise regression method. The root mean square error of prediction in a QSAR study using entropy, genetic algorithm and stepwise regression using multiple linear regressions model for training set and test set were 0.3433, 0.3591 and 0.5500, 0.4326 and 0.6373, 0.6672, respectively.

Original languageEnglish (US)
Pages (from-to)1787-1798
Number of pages12
JournalMolecular Physics
Issue number17
StatePublished - Oct 2009


  • Feature selection
  • Fuzzy entropy
  • Genetic algorithm
  • Multiple linear regressions
  • Quantitative structure-activity relationships
  • Regression

ASJC Scopus subject areas

  • Biophysics
  • Molecular Biology
  • Condensed Matter Physics
  • Physical and Theoretical Chemistry


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