Building a Rule-Based Classifier-A Fuzzy-Rough Set Approach

• Published in: IEEE transactions on knowledge and data engineering 2010-05, Vol.22 (5), p.624-638
• Main Authors: Suyun Zhao, Tsang, E.C.C., Degang Chen, Xizhao Wang
• Format: Article
• Language: English
• Subjects: Algorithm design and analysis; Algorithms; Applied sciences; Artificial intelligence; Buildings; Classification tree analysis; Classifiers; Computer science; control theory; systems; Construction; Data processing. List processing. Character string processing; Exact sciences and technology; Fuzzy sets; fuzzy-rough hybrids; IF-THEN rule; Invariants; Knowledge representation; Knowledge-based systems; Machine learning; Mathematical analysis; Mathematical models; Memory organisation. Data processing; Pattern recognition; Robust control; Robustness; Rule induction; rule-based classifier; Software; Studies
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2009.118

The fuzzy-rough set (FRS) methodology, as a useful tool to handle discernibility and fuzziness, has been widely studied. Some researchers studied on the rough approximation of fuzzy sets, while some others focused on studying one application of FRS: attribute reduction (i.e., feature selection). However, constructing classifier by using FRS, as another application of FRS, has been less studied. In this paper, we build a rule-based classifier by using one generalized FRS model after proposing a new concept named as ¿consistence degree¿ which is used as the critical value to keep the discernibility information invariant in the processing of rule induction. First, we generalized the existing FRS to a robust model with respect to misclassification and perturbation by incorporating one controlled threshold into knowledge representation of FRS. Second, we propose a concept named as ¿consistence degree¿ and by the strict mathematical reasoning, we show that this concept is reasonable as a critical value to reduce redundant attribute values in database. By employing this concept, we then design a discernibility vector to develop the algorithms of rule induction. The induced rule set can function as a classifier. Finally, the experimental results show that the proposed rule-based classifier is feasible and effective on noisy data.