An Effective Combination of Multiple Classifiers for Toxicity Prediction

Gongde Guo, Daniel Neagu, Xuming Huang, Yaxin Bi

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationFuzzy Systems and Knowledge Discovery Lecture Notes in Computer Science
ISBN (Print)978-3-540-45916-3
Publication statusPublished (in print/issue) - 2006


  • Classification
  • Multiple Classifiers
  • Toxicity Prediction


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