Learning in an Inconsistent World: Rule Selection in AQ18
Kenneth A. Kaufman, Ryszard S. Michalski · 1999
In concept learning and data mining tasks, the learner is typically faced with a choice of many possible hypotheses generalizing the input data. If one can assume that training data contains no noise, then the primary conditions a hypothesis must satisfy are consistency and completeness with regard to the data. In real-world applications, however, data are often noisy, and the insistence on the full completeness and consistency of the hypothesis is no longer valid. In such situations, the problem is to determine a hypothesis that represents the "best" trade-off between completeness and consistency. This paper presents an approach to this problem in which a learner seeks rules optimizing a rule quality criterion that combines the rule coverage (a measure of completeness) and training accuracy (a measure related to inconsistency). These factors are combined into a single rule quality measure, through a lexicographical evaluation functional (LEF). The method has been implemented in the AQ...