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An Effective Combination of Multiple Classifiers for Toxicity Prediction

  • Gongde Guo
  • Daniel Neagu
  • Xuming Huang
  • Yaxin Bi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)

Abstract

The performance of individual classifiers applied to complex data sets has for predictive toxicology a significant importance. An investigation was conducted to improve classification performance of combinations of classifiers. For this purpose some representative classification methods for individual classifier development have been used to assure a good range for model diversity. The paper proposes a new effective multi-classifier system based on Dempster’s rule of combination of individual classifiers. The performance of the new method has been evaluated on seven toxicity data sets. The classification accuracy of the proposed combination models achieved, according to our initial experiments, 2.97% better average than that of the best individual classifier among five classification methods (Instance-based Learning algorithm, Decision Tree, Repeated Incremental Pruning to Produce Error Reduction, Multi-Layer Perceptrons and Support Vector Machine) studied.

Keywords

Support Vector Machine Class Label Mass Function Feature Subset Combination Method 
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 2006

Authors and Affiliations

  • Gongde Guo
    • 1
    • 2
  • Daniel Neagu
    • 2
  • Xuming Huang
    • 1
    • 2
  • Yaxin Bi
    • 3
  1. 1.Dept. of Computer ScienceFujian Normal Univ.FuzhouChina
  2. 2.Dept. of ComputingUniv. of BradfordBradfordUK
  3. 3.School of Computing and MathematicsUniv. of UlsterUK

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