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

Kun Zhang, Zujia Xu, Jing Peng, Bill Buckles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005
Pages817-820
Number of pages4
DOIs
Publication statusPublished - 1 Dec 2005
Event5th IEEE International Conference on Data Mining, ICDM 2005 - Houston, TX, United States
Duration: 27 Nov 200530 Nov 2005

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other5th IEEE International Conference on Data Mining, ICDM 2005
CountryUnited States
CityHouston, TX
Period27/11/0530/11/05

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Zhang, K., Xu, Z., Peng, J., & Buckles, B. (2005). Learning through changes: An empirical study of dynamic behaviors of probability estimation trees. In Proceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005 (pp. 817-820). [1565790] (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDM.2005.88