Machine Learning
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
A Hierarchical Neural Network Document Classifier with Linguistic Feature Selection
Applied Intelligence
A proposed method for learning rule weights in fuzzy rule-based classification systems
Fuzzy Sets and Systems
International Journal of Remote Sensing
Fuzzifying Gini Index based decision trees
Expert Systems with Applications: An International Journal
A neural network-based multi-agent classifier system
Neurocomputing
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Data Mining with Decision Trees: Theroy and Applications
Data Mining with Decision Trees: Theroy and Applications
Fuzzy SLIQ Decision Tree Algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolving Fuzzy-Rule-Based Classifiers From Data Streams
IEEE Transactions on Fuzzy Systems
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In this paper, we propose a hierarchical fuzzy clustering decision tree (HFCDT) for the classification problem with large number of classes and continuous attributes. The HFCDT combines a division-degree matrix based hierarchal clustering technique with the entropy-based C4.5 decision tree algorithm. A hierarchical clustering concept is introduced to achieve a finer fuzzy partition. The hierarchical clustering technique splits the data set into leaf clusters using splitting attributes based on a division-degree matrix and fuzzy rules. The leaf clusters consisting of the data of more than one class will be further classified using the C4.5 algorithm. We have successfully applied the HFCDT for classifying recipes of the working wafers in an ion implanter, and compared the classification results and the training time with the existing software See5 and CART. The comparison results show that the HFCDT not only performs better than See5 and CART in the aspect of 10-fold cross validation for the average of total classification error rates but also consumes less training time. Thus, HFCDT obtains a very successful classification result. This also demonstrates why the hierarchical clustering technique helps reduce the computational complexity of the C4.5 algorithm.