Data mining in finance: advances in relational and hybrid methods
Data mining in finance: advances in relational and hybrid methods
Data mining: concepts and techniques
Data mining: concepts and techniques
Using Multi-Attribute Predicates for Mining Classification Rules
COMPSAC '98 Proceedings of the 22nd International Computer Software and Applications Conference
Rule Reduction over Numerical Attributes in Decision Tree Using Multilayer Perceptron
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Application of an enhanced decision tree learning approach for prediction of petroleum production
Engineering Applications of Artificial Intelligence
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The decision-tree learning algorithms, e.g., C5, are good at dataset classification. But those algorithms usually work with only one attribute at a time. The dependencies among attributes are not considered in those algorithms. Unfortunately, in the real world, most datasets contain attributes, which are dependent. Generally, these dependencies are classified into two types: categorical-type and numerical-type dependencies. Thus, it is very important to construct a model to discover the dependencies among attributes, and to improve the accuracy of the decision-tree learning algorithms. Neural network model is a good choice to concern with these two types of dependencies. In this paper, we propose a Neural Decision Tree (NDT) model to deal with the problems described above. NDT model combines the neural network technologies and the traditional decision-tree learning capabilities to handle the complicated and real cases. The experimental results show that the NDT model can significantly improve the accuracy of C5.