An effective combination based on class-wise expertise of diverse classifiers for predictive toxicology data mining

Authors:
Daniel Neagu;Gongde Guo;Shanshan Wang
Affiliations:
Dept. of Computing, Univ. of Bradford, Bradford, UK;Dept. of Computing, Univ. of Bradford, Bradford, UK;Dept. of Computer Science, Nanjing Univ. of Aeronautics and Astronautics, China
Venue:
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Year:
2006

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Abstract

This paper presents a study on the combination of different classifiers for toxicity prediction. Two combination operators for the Multiple-Classifier System definition are also proposed. The classification methods used to generate classifiers for combination are chosen in terms of their representability and diversity and include the Instance-based Learning algorithm (IBL), Decision Tree learning algorithm (DT), Repeated Incremental Pruning to Produce Error Reduction (RIPPER), Multi-Layer Perceptrons (MLPs) and Support Vector Machine (SVM). An effective approach of combining class-wise expertise of diverse classifiers is proposed and evaluated on seven toxicity data sets. The experimental results show that the performance of the combined classifier based on our approach over seven data sets can achieve 69.24% classification accuracy on average, which is better than that of the best classifier (generated by MLP) and four combination schemes studied.