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Abstract

Abstract. While many constructive induction algorithms focus on generating new binary attributes, this paper explores novel methods of constructing nominal and numeric attributes. We propose a new constructive operator, X -of-N . An X-of-N representation is a set containing one or more attribute-value pairs. For a given instance, the value of an X -of-N representation corresponds to the number of its attribute-value pairs that are true of the instance. A single X -of-N representation can directly and simply represent any concept that can be represented by a single conjunctive, a single disjunctive, or a single M-of-N representation commonly used for constructive induction, and the reverse is not true. In this paper, we describe a constructive decision tree learning algorithm, called XofN. When building decision trees, this algorithm creates one X -of-N representation, either as a nominal attribute or as a numeric attribute, at each decision node. The construction of X -of-N representations is carried out by greedily searching the space defined by all the attribute-value pairs of a domain. Experimental results reveal that constructing X -of-N attributes can significantly improve the performance of decision tree learning in both artificial and natural domains in terms of higher prediction accuracy and lower theory complexity. The results also show the performance advantages of constructing X -of-N attributes over constructing conjunctive, disjunctive, or M-of-N representations for decision tree learning.

Classification, Regression, and Survival Rule Induction with Complex and M-of-N Elementary Conditions

“…To induce the M-of-N conditions, greedy search [45,46], genetic algorithms [47], and constructive induction [48][49][50] have been applied. There are articles that present the idea of inducing M-of-N conditions from trained neural networks [51,52] and decision trees [50,53]. Some algorithms assume that the rule premise contains only M-of-N conditions.…”