Rule Optimization Via SG-TRUNC Method
Jianping Zhang, Ryszard S. Michalski · 1989
Most inductive learning systems generate complete and consistent descriptions. In order to achieve completeness and consistency in the presence of noise or iraprecision, one may generate overly complex and detailed descriptions. Such descriptions, however, may not perform well in future cases and suffer the disadvantage of excessive complexity. This is the well known phenomenon of overfitting. In this paper, a rule optimization method called SG-TRUNC is described and evaluated experimentally. SG-TRUNC improves previous TRUNC methods and has been implemented in a more efficient way. In the method, an optimized description is obtained through a sequence of generalization and/or specialization operations performed on a complete and consistent concept description. The operations applied always simplify a description.