An incremental learning algorithm of ensemble classifier systems

In this paper, we propose an incremental learning model for ensemble classifier systems. In the proposed model, the number of classifiers is predetermined and fixed during the learning, and all classifiers are updated at every learning stage based on an extended algorithm of AdaBoost.M1. A neural network model called Resource Allocating Network with Long-Term Memory (RAN-LTM), which has been developed to realize stable incremental learning, is adopted as a classifier. We also propose a new method to update the classifier weights in the weighted majority voting under the one-pass incremental learning situations. In the experiments, first we verify that the proposed model can learn incrementally without serious forgetting and that the performance is not influenced seriously by the size of a training subset given at every learning stage. Then, through a comparison with Resource Allocating Network (RAN), RAN-LTM, and AdaBoost.M1, we demonstrate that the proposed incremental ensemble classifier system has comparable performance with a batch-learning ensemble classifier system, and that it outperforms both batch-learning and incremental-learning single-classifier systems.