C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Self-organizing maps
Data mining: concepts and techniques
Data mining: concepts and techniques
Machine Learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Forward and Backward Selection in Regression Hybrid Network
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Optimizing a Multiple Classifier System
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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Recently, multiple classifier systems (MCS) have been used for practical applications to improve classification accuracy. Self-generating neural networks (SGNN) are one of the suitable base-classifiers for MCS because of their simple setting and fast learning. However, the computational cost of the MCS increases in proportion to the number of SGNN. In an earlier paper, we proposed a pruning method for the structure of the SGNN in the MCS to reduce the computational cost. In this paper, we propose a novel pruning method for effective processing. The pruning method is constructed from an on-line pruning method and an off-line pruning method. We implement the pruned MCS with two sampling methods. Experiments have been conducted to compare the pruned MCS with the unpruned MCS, the MCS based on C4.5, and k- nearest neighbor method. The results show that the pruned MCS can improve its classification accuracy as well as reducing the computational cost.