Simplifying decision trees

Author: QUINLAN J.R.

Source: International Journal of Human-Computer Studies, Volume 51, Number 2, August 1999 , pp. 497-510(14)

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Abstract:

Many systems have been developed for constructing decision trees from collections of examples. Although the decision trees generated by these methods are accurate and efficient, they often suffer the disadvantage of excessive complexity and are therefore incomprehensible to experts. It is questionable whether opaque structures of this kind can be described as knowledge, no matter how well they function. This paper discusses techniques for simplifying decision trees while retaining their accuracy. Four methods are described, illustrated, and compared on a test-bed of decision trees from a variety of domains. Copyright 1999 Academic Press

Language: English

Document Type: Research article

Affiliations: Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 545 Technology Square, Cambridge, MA, 02139, U.S.A.

Publication date: 1999-08-01