Improving Accuracy of Incorrect Domain Theories

An approach to improve accuracy of incorrect domain theories is presented that learns concept descriptions from positive and negative examples of the concept. The method uses the available domain theory, that might be both overly general and overly specific, to group training examples before attempting concept induction. GENTRE is a system that has been implemented to test the performance of the method. GENTRE is not limited to variable-free, function-free or non-recursive domains as many other approaches. In the paper we present results from experiments in three different domains and compare the performance of GENTRE with that of ID3 and IOU. The learned concept descriptions are consistent with training examples and have an improved classification accuracy relative to the original domain theory.

[1]  Jude Shavlik,et al.  Refinement ofApproximate Domain Theories by Knowledge-Based Neural Networks , 1990, AAAI.

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

[3]  William W. Cohen.  Abductive explanation-based learning: A solution to the multiple inconsistent explanation problem , 1992, Machine Learning.

[4]  Björn Gambäck,et al.  EBL2: An Approach To Automatic Lexical Acquisition , 1992, COLING.

[5]  Francesco Bergadano,et al.  A Knowledge Intensive Approach to Concept Induction , 1988, ML Workshop.

[6]  Raymond J. Mooney,et al.  Induction Over the Unexplained: Integrated Learning of Concepts with Both Explainable and Conventional Aspects , 1989, ML.

[7]  Lars Asker.  Partial explanations as a basis for learning , 1994 .

[8]  G. Plotkin.  Automatic Methods of Inductive Inference , 1972 .

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

[10]  Raymond J. Mooney,et al.  First-Order Theory Revision , 1991, ML.

[11]  Ryszard S. Michalski,et al.  A theory and methodology of inductive learning , 1993 .

[12]  Andrea Pohoreckyj Danyluk,et al.  Finding New Rules for Incomplete Theories: Explicit Biases for Induction with Contextual Information , 1989, ML.

[13]  Lars Asker.  Using Partial Explanations: An Approach to Solving the Incomplete Theory Problem in EBL , 1991, SCAI.

[14]  Kamal M. Ali,et al.  Augmenting Domain Theory for Explanation-Based Generalization , 1989, ML.

[15]  William W. Cohen.  Learning Approximate Control Rules of High Utility , 1990, ML.

[16]  Masamichi Shimura,et al.  Refining a Relational Theory with Multiple Faults in the Concept and Subconcepts , 1992, ML.