An Inductive Learning Algorithm with a Partial Completeness and Consistence via a Modified Set Covering Problem

Abstract

We present an inductive learning algorithm that allows for a partial completeness and consistence, i.e. that derives classification rules correctly describing, e.g, most of the examples belonging to a class and not describing most of the examples not belonging to this class. The problem is represented as a modification of the set covering problem that is… (More)
DOI: 10.1007/11550907_105

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