The Role of Occam‘s Razor in Knowledge Discovery
Data Mining and Knowledge Discovery
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Application of argument based machine learning to law
ICAIL '05 Proceedings of the 10th international conference on Artificial intelligence and law
Arguing and explaining classifications
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Argument based machine learning in a medical domain
Proceedings of the 2006 conference on Computational Models of Argument: Proceedings of COMMA 2006
Arguing and explaining classifications
ArgMAS'07 Proceedings of the 4th international conference on Argumentation in multi-agent systems
Argument-Based machine learning
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
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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.