Abstract
Problem partitioning strategies such as sequential covering are commonly used in rule learning algorithms, such that the task of finding a complete rule base is reduced to a sequence of subproblems. In this scenario, each solution to a subproblem consists of adding a single rule to the entire set. In this paper, we propose an alternative for rule learning based on the use of an adaptive formalism whose behavior is determined by a dynamic set of rules. Preliminary results yield a compact yet significantly comprehensible rule set, as well as an efficient model representation, from raw data using adaptive techniques. To this end, we include further discussions regarding features and enhancements to be contemplated in future works.
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