DCL: A disjunctive learning algorithm for rule extraction

Abstract

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 rules than most other algorithms used for inductive concept learning, and also can classify considerably more unseen examples than conjunctive algorithms.