AN INCREMENTAL LEARNING-SYSTEM FOR IMPRECISE AND UNCERTAIN KNOWLEDGE DISCOVERY

Discovering knowledge from databases in order to classify new patterns is an interesting field for machine learning methods. Particularly, r ule 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 sy stem that generates imprecise and uncertain IF-THEN rules from data re cords. It allows the incremental maintenance of the knowledge base wit h a minimal overhead. The precision analysis with real world data sets , and the complexity analysis are used to compare this system with exi sting ones and to prove the usefulness of fuzzy knowledge representati on. (C) 1998 Elsevier Science Inc. All rights reserved.