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A Study on Feature Selection for Toxicity Prediction

  • Gongde Guo
  • Daniel Neagu
  • Mark T. D. Cronin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3614)

Abstract

The increasing amount and complexity of data used in predictive toxicology calls for efficient and effective feature selection methods in data pre-processing for data mining. In this paper, we propose a kNN model-based feature selection method (kNNMFS) aimed at overcoming the weaknesses of ReliefF method. It modifies the ReliefF method by: (1) using a kNN model as the starter selection aimed at choosing a set of more meaningful representatives to replace the original data for feature selection; (2) integration of the Heterogeneous Value Difference Metric to handle heterogeneous applications – those with both ordinal and nominal features; and (3) presenting a simple method of difference function calculation. The performance of kNNMFS was evaluated on a toxicity data set Phenols using a linear regression algorithm. Experimental results indicate that kNNMFS has a significant improvement in the classification accuracy for the trial data set.

Keywords

Feature Selection Feature Selection Method Feature Subset Selection Relative Absolute Error Toxicity Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Gongde Guo
    • 1
  • Daniel Neagu
    • 1
  • Mark T. D. Cronin
    • 2
  1. 1.Department of ComputingUniversity of BradfordBradfordUK
  2. 2.School of Pharmacy and ChemistryLiverpool John Moores UniversityUK

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