Argument Based Rule Learning
Martin Možina, Jure Žabkar, Ivan Bratko · European Conference on Artificial Intelligence · 2006
We present a novel approach to machine learning, called ABML (argumentation based ML). This approach combines machine learning from examples with concepts from the field of argumentation. The idea is to provide expert's arguments, or reasons, for some of the learning examples. We require that the theory induced from the examples explains the examples in terms of the given reasons. Thus arguments constrain the combinatorial search among possible hypotheses, and also direct the search towards hypotheses that are more comprehensible in the light of expert's background knowledge. In this paper we implement ABCN2 as an extension of the CN2 rule learning algorithm, and analyze its advantages in comparison with the original CN2 algorithm.