Learning decision rules using adaptive technologies: a hybrid approach based on sequential covering

Sequential covering strategies are commonly used in rule learning algorithms, as they apply separate and conquer approaches, in which the task of finding a complete rule base is reduced to a sequence of subproblems; each solution to a subproblem consists in adding a single rule. We propose an alternative for rule learning, namely the use of adaptive devices whose behavior is defined by a dynamic set of rules. In order to integrate features from rule learning methods and adaptive technologies, this paper presents a hybrid approach to learn classification rules, such that the set of rules is dynamically modified by adding or removing rules. The results have been promising towards applying adaptive technology to learn rules directly from data.