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 solved by a greedy algorithm. The approach is illustrated on some medical data. © Springer-Verlag Berlin Heidelberg 2005.