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Simplifying Decision Trees (1986) [2533 citations — 3 self]

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@MISC{Quinlan86simplifyingdecision,
    author = {J. R. Quinlan},
    title = {Simplifying Decision Trees},
    year = {1986}
}

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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 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 dis- cusses 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.

Citations

2577	Classification and Regression Trees – Breiman, Friedman, et al. - 1984
111	A Guide to Expert Systems – WATERMAN - 1985
50	Model-directed learning of production rules – Buchanan, Mitchell - 1978
49	Inductive knowledge acquisition: a case study – Quinlan, Compton, et al. - 1986
27	Experiments in automatic learning of medical diagnostic rules – Kononenko, Bratko, et al. - 1984
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5	Expert systems in the 1980s – Feigenbaum - 1981
2	Tables for Testing Significance in a 2 x 2 Contingency Table – Finney, Latscha, et al. - 1963
1	An experimental comparison of two learning programs in three medical domains – Lavra, Mozeti, et al. - 1986
1	Learning by being told and learning by examples: an experimental comparison of the two methods of knowledge acquisition in the context of developing an expert system for soybean disease diagnosis – Michalski, Chilausky - 1980
1	Inductive rule generation in the context of the Fifth Generation – Michie - 1983