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Learning through changes: an empirical study of dynamic behaviors of probability estimation trees

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4 Author(s)
Kun Zhang ; Electr. Eng. & Comput. Sci. Dept., Tulane Univ., New Orleans, LA, USA ; Zujia Xu ; Jing Peng ; Buckles, B.

In practice, learning from data is often hampered by the limited training examples. In this paper, as the size of training data varies, we empirically investigate several probability estimation tree algorithms over eighteen binary classification problems. Nine metrics are used to evaluate their performances. Our aggregated results show that ensemble trees consistently outperform single trees. Confusion factor trees(CFT) register poor calibration even as training size increases, which shows that CFTs are potentially biased if data sets have small noise. We also provide analysis on the observed performance of the tree algorithms.

Published in:

Data Mining, Fifth IEEE International Conference on

Date of Conference:

27-30 Nov. 2005