An Iterative Growing and Pruning Algorithm for Classification Tree Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
Design Smart NNTrees Based on the R"-Rule
AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 2
A Nonparametric Partitioning Procedure for Pattern Classification
IEEE Transactions on Computers
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The plasticity-stability dilemma is a key problem for learning with data streams. On the one hand, the learner must be plastic enough to adapt to the new data. On the other hand, it must be stable enough to integrate information. In this paper, we try to resolve this problem using neural network trees (NNTrees). An NNTree is a decision tree (DT) with each non-terminal node containing an expert neural network (ENN). The NNTrees are plastic because they can adapt to the new data through retraining of the ENNs and/or through generation of new nodes. The NNTrees are also stable because retraining is performed partially and locally. In this paper, we propose an algorithm that can grow NNTrees effectively and efficiently. Experiments with several public databases show that the NNTrees obtained by the proposed methods are comparable with the NNTrees or DTs obtained with all data provided all-at-once.