期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 24, 期 8, 页码 1491-1505出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2011.67
关键词
Learning; uncertainty; sample selection; fuzzy decision tree
类别
资金
- National Natural Science Foundation of China [61170040]
- Natural Science Foundation of Hebei Province [F2008000635, F2012201023]
- key project of applied fundamental research of Hebei Province [08963522D]
- Education Department in Hebei Province
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 this mechanism is that the adjustment of the fuzzy decision tree is minimized when adding selected samples to the training set. This advantage is confirmed via the theoretical analysis of the leaf-nodes' frequency in the decision trees. The decision tree generated from the selected samples usually has a better performance than that from the original database. Furthermore, experimental results show that generalization ability of the tree based on our selection mechanism is far more superior to that based on random selection mechanism.
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