Simplifying decision trees
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TLDR
Techniques for simplifying decision trees while retaining their accuracy are discussed, described, illustrated, and compared on a test-bed of decision trees from a variety of domains.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.read more
Citations
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References
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Journal ArticleDOI
Classification and Regression Trees.
Journal ArticleDOI
Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
Book
A Guide to Expert Systems
TL;DR: Technical managers, professionals, and researchers who are considering the implementation or application of expert systems will find this book to be an authoritative, but accessible guide to the state-of-the-art.
Book
Pattern-directed inference systems
TL;DR: In this paper, the authors discuss a crop identification and acreage estimation case study, followed by rather brief discussions of five selected management problems: large area land use inventory and forest, snow-cover, geologic, and water-temperature mapping.