'Rule+Exception' Learning Based on Reduct
Wang Jue, Yao Yi, Wang Fei · Chinese Journal of Computers · 2005
Machine learning normally focuses on and treats exceptions not covered by the rules as noise.In many applications,it is necessary to have not only rules describing the observations but also explicit and explainable representation of exceptions.Exceptions may be an important type of knowledge in these applications,such as intelligence analysis and security warning.Based on the notion of Reducts,a theoretical framework for learning rule+exception knowledge is presented within the context of symbolic learning.The basic components and the main issues of the framework are discussed.