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C4.5: programs for machine learning
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Data mining: practical machine learning tools and techniques with Java implementations
Globally Optimal Fuzzy Decision Trees for Classification and Regression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
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Hierarchical Mixtures of Experts and the EM Algorithm
Hierarchical Mixtures of Experts and the EM Algorithm
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Converting a trained neural network to a decision tree dectext - decision tree extractor
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Online adaptive decision trees
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IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Class Specific Fuzzy Decision Trees for Mining High Speed Data Streams
Fundamenta Informaticae
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Information Processing and Management: an International Journal
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ACM SIGOPS Operating Systems Review
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Recently we have shown that decision trees can be trained in the online adaptive (OADT) mode (Basak, 2004), leading to better generalization score. OADTs were bottlenecked by the fact that they are able to handle only two-class classification tasks with a given structure. In this article, we provide an architecture based on OADT, ExOADT, which can handle multiclass classification tasks and is able to perform function approximation. ExOADT is structurally similar to OADT extended with a regression layer. We also show that ExOADT is capable not only of adapting the local decision hyperplanes in the nonterminal nodes but also has the potential of smoothly changing the structure of the tree depending on the data samples. We provide the learning rules based on steepest gradient descent for the new model ExOADT. Experimentally we demonstrate the effectiveness of ExOADT in the pattern classification and function approximation tasks. Finally, we briefly discuss the relationship of ExOADT with other classification models.