Accession Number : ADA183615

Title :   Simplifying Decision Trees,

Corporate Author : MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB

Personal Author(s) : Quinlan,J R

PDF Url : ADA183615

Report Date : Dec 1986

Pagination or Media Count : 17

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 that can render them 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 without compromising their accuracy. Four methods are described, illustrated, and compared on a test-bed of decision trees from a variety of domains.

Descriptors :   *ARTIFICIAL INTELLIGENCE, *DECISION MAKING, DECISION THEORY, FAULT TREE ANALYSIS, ACCURACY, TEST BEDS, INFORMATION PROCESSING

Subject Categories : Cybernetics
      Operations Research

Distribution Statement : APPROVED FOR PUBLIC RELEASE