
Statistics Papers
Document Type
Journal Article
Date of this Version
2001
Publication Source
Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume
KDD '01
Start Page
27
Last Page
36
DOI
10.1145/502512.502522
Abstract
This paper is concerned with the construction of regression and classification trees that are more adapted to data mining applications than conventional trees. To this end, we propose new splitting criteria for growing trees. Conventional splitting criteria attempt to perform well on both sides of a split by attempting a compromise in the quality of fit between the left and the right side. By contrast, we adopt a data mining point of view by proposing criteria that search for interesting subsets of the data, as opposed to modeling all of the data equally well. The new criteria do not split based on a compromise between the left and the right bucket; they effectively pick the more interesting bucket and ignore the other.As expected, the result is often a simpler characterization of interesting subsets of the data. Less expected is that the new criteria often yield whole trees that provide more interpretable data descriptions. Surprisingly, it is a "flaw" that works to their advantage: The new criteria have an increased tendency to accept splits near the boundaries of the predictor ranges. This so-called "end-cut problem" leads to the repeated peeling of small layers of data and results in very unbalanced but highly expressive and interpretable trees.
Keywords
Boston housing data, CART, Pima Indians diabetes data, splitting criteria
Recommended Citation
Buja, A., & Lee, Y. (2001). Data Mining Criteria for Tree-Based Regression and Classification. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '01 27-36. http://dx.doi.org/10.1145/502512.502522
Date Posted: 27 November 2017
This document has been peer reviewed.
Comments
At the time of publication, author Andreas Buja was affiliated with AT&T Labs, Florham Park, NJ. Currently (August 2016), he is a faculty member at the Statistics Department at the University of Pennsylvania.