Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Evidence Theory and Its Applications
Evidence Theory and Its Applications
Combining Multiple Learning Strategies for Effective Cross Validation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Combining Classifiers of Pesticides Toxicity through a Neuro-fuzzy Approach
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Using kNN model for automatic feature selection
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
A neural network classifier based on Dempster-Shafer theory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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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.