An incremental learning system for imprecise and uncertain knowledge discovery

Abstract

Discovering knowledge from databases in order to classify new patterns is an interesting field for machine learning methods. Particularly, rule induction approaches constitute prominent machine learning methods that lead to avoid the disadvantages of the decision tree. The fuzzy incremental production rule (FIPR) based system is a rule induction system that generates imprecise and uncertain IF-THEN rules from data records. It allows the incremental maintenance of the knowledge base with a minimal overhead. The precision analysis with real world data sets, and the complexity analysis are used to compare this system with existing ones and to prove the usefulness of fuzzy knowledge representation.