论文信息 - Rule optimisation and theory optimisation : Heuristic search strategies for data driven machine learning

Rule optimisation and theory optimisation : Heuristic search strategies for data driven machine learning

Previous implementations of the Aq algorithm have used rule optimisation search strategies to attempt to develop optimal classification procedures. These strategies involve generating successive characteristic descriptions each of which is individually of maximal value. This is contrasted with theory optimisation search strategies which, instead, generate successive complete classification procedures from which those with the maximal value are selected. These two strategies have been applied to the domain of the diagnosis of Immunoglobulin A Nephropathy disease. The theory optimisation strategy was observed to out perform the rule optimisation strategy.

Geoffrey I. Webb | G. Webb

[1] J. Ross Quinlan,et al. Induction of Decision Trees , 1986, Machine Learning.

[2] J. Ross Quinlan,et al. Simplifying decision trees , 1987, Int. J. Hum. Comput. Stud..

[3] Geoffrey I. Webb. Techniques for Efficient Empirical Induction , 1988, Australian Joint Conference on Artificial Intelligence.

[4] Ryszard S. Michalski,et al. A Theory and Methodology of Inductive Learning , 1983, Artificial Intelligence.

[5] James Burton Larson,et al. Inductive inference in the variable valued predicate logic system vl21: methodology and computer implementation. , 1977 .

[6] Ryszard S. Michalski,et al. Synthesis of Optimal and Quasi-Optimal Variable-Valued Logic Formulas , 1975 .

[7] Patrick Henry Winston,et al. Learning structural descriptions from examples , 1970 .