Abstract

This article presents an incremental algorithm for inducing decision trees equivalent to those formed by Quinlan's nonincremental ID3 algorithm, given the same training instances. The new algorithm, named ID5R, lets one apply the ID3 induction process to learning tasks in which training instances are presented serially.

Citations

4404	The Mathematical Theory of Communication - Shannon, Weaver - 1963
3143	Classification and Regression Trees - Breiman, Friedman, et al. - 1984
2888	Induction of Decision Trees - Quinlan - 1986
569	DH: Knowledge Acquisition via Incremental Conceptual Clustering - Fisher - 1987
292	Learning Efficient Classification Procedures and their Application to Chess End Games - Quinlan - 1983
138	Version Spaces: An Approach to Concept Learning - Mitchell - 1978
107	Connectionist Expert Systems - Gallant - 1988
104	Learning by being told and learning from 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
86	Pattern recognition: human and mechanical - Watanabe - 1985
77	A case study of incremental concept induction - Schlimmer, Fisher - 1986
65	Id5: An incremental id3 - Utgoff - 1988
58	Decision Trees and Diagrams - Moret - 1982
55	Model-directed learning of production rules - Buchanan, Mitchell - 1978
46	Learning by statistical cooperation of self-interested neuronlike computing elements." Humn Neurobiology - Barto - 1985
26	An empirical comparison of genetic and decisiontree classifiers - Quinlan - 1988
26	Multilevel Counterfactuals for Generalizations of Relational Concepts and Productions - Vere - 1980
12	The characteristic selection problem in recognition systems - Lewis - 1962
5	Improved training via incremental learning - Utgoff - 1989
4	Discrete decision theory: Manipulations - Cockett - 1987
1	A theory and methodology of inductive learning (pp. 83-134 - Michalski - 1983
1	Incremental Induction of Decision Trees 26 - Granger - 1986