DLGref 2 : Techniques for Inductive Knowledge Refinement

This paper describes and evaluates machine learning techniques for knowledge-base refinement. These techniques are central to Einstein, a knowledge acquisition system that enables a human expert to collaborate with a machine learning system at all stages of the knowledge-acquisition cycle. Experimental evaluation demonstrates that the knowledge-base refinement techniques are able to significantly increase the accuracy of nontrivial expert systems in a wide variety of domains.

[1]  Geoffrey I. Webb,et al.  Rule optimisation and theory optimisation : Heuristic search strategies for data driven machine learning , 2005 .

[2]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[3]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[4]  Geoffrey I. Webb.  Control , capabilities and communication : Three key issues for machine – expert collaborative knowledge-acquisition , 1993 .

[5]  Geoffrey I. Webb,et al.  Inducing diagnostic rules for glomerular disease with the DLG machine learning algorithm , 1992, Artif. Intell. Medicine.

[6]  Geoffrey I. Webb.  MAN-MACHINE COLLABORATION FOR KNOWLEDGE ACQUISITION , 1992 .

[7]  Michael J. Pazzani,et al.  Detecting and correcting errors in rule-based expert systems: an integration of empirical and explanation-based learning , 1991 .

[8]  David C. Wilkins,et al.  Improving the Performance of Inconsistent Knowledge Bases via Combined Optimization Method , 1991, ML.

[9]  Raymond J. Mooney,et al.  Changing the Rules: A Comprehensive Approach to Theory Refinement , 1990, AAAI.

[10]  Stephen Muggleton,et al.  Efficient Induction of Logic Programs , 1990, ALT.

[11]  Ryszard S. Michalski,et al.  Incremental learning of concept descriptions: A method and experimental results , 1988 .

[12]  Allen Ginsberg,et al.  Automatic Refinement of Expert System Knowledge Bases , 1988 .

[13]  Sholom M. Weiss,et al.  Automatic Knowledge Base Refinement for Classification Systems , 1988, Artif. Intell..

[14]  Richard A. Caruana,et al.  The automatic training of rule bases that use numerical uncertainty representations , 1987, Int. J. Approx. Reason..

[15]  J. Ross Quinlan,et al.  Generating Production Rules from Decision Trees , 1987, IJCAI.

[16]  David C. Wilkins,et al.  On Debugging Rule Sets When Reasoning Under Uncertainty , 1986, AAAI.

[17]  Sylvian R. Ray,et al.  Rule Refinement Using the Probabilistic Rule Generator , 1986, AAAI.

[18]  Tom M. Mitchell,et al.  Representation and Use of Explicit Justifications for Knowledge Base Refinements , 1985, IJCAI.

[19]  Roy Rada,et al.  Gradualness Facilitates Knowledge Refinement , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  A. Kitchen,et al.  Knowledge based systems in artificial intelligence , 1985, Proceedings of the IEEE.