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

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). How...

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Bibliographic Details
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
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Summary: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.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2009.118