DCL: A disjunctive learning algorithm for rule extraction

Date

1999-01-01

Author

Abu-Soud, SM
Tolun, MR

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Most concept learning algorithms are conjunctive algorithms, i.e. generate production rules that include AND-operators only. This paper examines the induction of disjunctive concepts or descriptions. We present an algorithm, called DCL, for disjunctive concept learning that partitions the training data according to class descriptions. This algorithm is an improved version of our conjunctive learning algorithm, ILA. DCL generates production rules with AND/OR-operators from a set of training examples. This approach is particularly useful for creating multiple decision boundaries. We also describe application of DCL to a range of training sets with different number of attributes and classes. The results obtained show that DCL can produce fewer number of rule than most of the algorithms used for inductive concept learning, and also can classify considerably more unseen examples than conjunctive algorithms.

Subject Keywords

Rule extraction, Inductive algorithm, Integrate decision support system, Conjunctive algorithm, Disjunctive concept

URI

https://hdl.handle.net/11511/66070

Journal

MULTIPLE APPROACHES TO INTELLIGENT SYSTEMS, PROCEEDINGS

Collections

Department of Computer Engineering, Article

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Citation Formats

S. Abu-Soud and M. Tolun, “DCL: A disjunctive learning algorithm for rule extraction,” MULTIPLE APPROACHES TO INTELLIGENT SYSTEMS, PROCEEDINGS, pp. 669–678, 1999, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/66070.