Data preparation for data mining
Data preparation for data mining
Data mining in finance: advances in relational and hybrid methods
Data mining in finance: advances in relational and hybrid methods
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Effective Data Mining Using Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Handling Continuous-Valued Attributes in Decision Tree with Neural Network Modelling
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Decision-tree instance-space decomposition with grouped gain-ratio
Information Sciences: an International Journal
A neural network ensemble method with jittered training data for time series forecasting
Information Sciences: an International Journal
A modal learning adaptive function neural network applied to handwritten digit recognition
Information Sciences: an International Journal
Blind paraunitary equalization
Signal Processing
Constructing a decision tree from data with hierarchical class labels
Expert Systems with Applications: An International Journal
Moving towards efficient decision tree construction
Information Sciences: an International Journal
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
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 and adopt the greedy method to build the decision tree. The dependencies among attributes are not considered in those algorithms. Unfortunately, in the real world, most datasets contain attributes, which are dependent. Thus, the results generated by those algorithms are not the optimal learning results. However, it is a combinatorial explosion problem for considering multiple attributes at a time. So, it is very important to construct a model to efficiently discovery the dependencies among attributes, and to improve the accuracy and effectiveness of the decision tree learning algorithms. Generally, these dependencies are classified into two types: categorical-type and numerical-type dependencies. This paper proposes a Neural Decision Tree (NDT) model, to deal with these two kinds of dependencies. The NDT model combines the neural network technologies and the traditional decision-tree learning capabilities, to handle the complicated and real cases. According to the experiments on ten datasets from the UCI database repository, the NDT model can significantly improve the accuracy and effectiveness of C5.