In data mining, we emphasize the need for learning from huge, incomplete, and imperfect data sets. To handle noise in the problem domain, existing learning systems avoid overfitting the imperfect training examples by excluding insignificant patterns. The problem is that these systems use a limiting attribute-value language for representing the training examples and the induced knowledge. Moreover, some important patterns are ignored because they are statistically insignificant. In this article, we present a framework that combines Genetic Programming and Inductive Logic Programming to induce knowledge represented in various knowledge representation formalisms from noisy databases. The framework is based on a formalism of logic grammars, and it can specify the search space declaratively. An implementation of the framework, LOGENPRO (The Logic grammar based GENetic PROgramming system), has been developed. The performance of LOGENPRO is evaluated on the chess end-game domain. We compare LOGENPRO with FOIL and other learning systems in detail, and find its performance is significantly better than that of the others. This result indicates that the Darwinian principle of natural selection is a plausible noise handling method that can avoid overfitting and identify important patterns at the same time. Moreover, the system is applied to one real-life medical database. The knowledge discovered provides insights to and allows better understanding of the medical domains.
|Number of pages||12|
|Journal||Journal of the American Society for Information Science|
|Publication status||Published - 1 Jan 2000|