Evolving Fuzzy Min–Max Neural Network Based Decision Trees for Data Stream Classification

Learning from data streams is a challenging task which demands a learning algorithm with several high quality features. In addition to space complexity and speed requirements needed for processing the huge volume of data which arrives at high speed, the learning algorithm must have a good balance between stability and plasticity. This paper presents a new approach to induce incremental decision trees on streaming data. In this approach, the internal nodes contain trainable split tests. In contrast with traditional decision trees in which a single attribute is selected as the split test, each internal node of the proposed approach contains a trainable function based on multiple attributes, which not only provides the flexibility needed in the stream context, but also improves stability. Based on this approach, we propose evolving fuzzy min–max decision tree (EFMMDT) learning algorithm in which each internal node of the decision tree contains an evolving fuzzy min–max neural network. EFMMDT splits the instance space non-linearly based on multiple attributes which results in much smaller and shallower decision trees. The extensive experiments reveal that the proposed algorithm achieves much better precision in comparison with the state-of-the-art decision tree learning algorithms on the benchmark data streams, especially in the presence of concept drift.