Maximum Ambiguity-Based Sample Selection in Fuzzy Decision Tree Induction

@article{Wang2012MaximumAS,
  title={Maximum Ambiguity-Based Sample Selection in Fuzzy Decision Tree Induction},
  author={Xizhao Wang and Ling-Cai Dong and Jian-Hui Yan},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2012},
  volume={24},
  pages={1491-1505}
}
Sample selection is to select a number of representative samples from a large database such that a learning algorithm can have a reduced computational cost and an improved learning accuracy. This paper gives a new sample selection mechanism, i.e., the maximum ambiguity-based sample selection in fuzzy decision tree induction. Compared with the existing sample selection methods, this mechanism selects the samples based on the principle of maximal classification ambiguity. The major advantage of… CONTINUE READING