A neural network approach to discover attribute dependency for improving the performance of classification

@article{Yen2011ANN,
  title={A neural network approach to discover attribute dependency for improving the performance of classification},
  author={Show-Jane Yen and Yue-Shi Lee},
  journal={Expert Syst. Appl.},
  year={2011},
  volume={38},
  pages={12328-12338}
}
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… CONTINUE READING
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