Journal
IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume 23, Issue 2, Pages 369-386Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2014.2312983
Keywords
Classifier architectures; data streams; evolving fuzzy rule; base classifier; feature weighting; online learning; rule pruning; rule recall
Funding
- Austrian fund for promoting scientific research (FWF) [I328-N23]
- research programme at the LCM GmbH as part of a K2 Project
- Austrian COMET-K2 programme
- Austrian federal government
- federal state of Upper Austria
- Johannes Kepler University
- K2-COMET Consortium
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In this paper, a novel evolving fuzzy-rule-based classifier, termed parsimonious classifier (pClass), is proposed. pClass can drive its learning engine from scratch with an empty rule base or initially trained fuzzy models. It adopts an open structure and plug and play concept where automatic knowledge building, rule-based simplification, knowledge recall mechanism, and soft feature reduction can be carried out on the fly with limited expert knowledge and without prior assumptions to underlying data distribution. In this paper, three state-of-the-art classifier architectures engaging multi-input-multi-output, multimodel, and round robin architectures are also critically analyzed. The efficacy of the pClass has been numerically validated by means of real-world and synthetic streaming data, possessing various concept drifts, noisy learning environments, and dynamic class attributes. In addition, comparative studies with prominent algorithms using comprehensive statistical tests have confirmed that the pClass delivers more superior performance in terms of classification rate, number of fuzzy rules, and number of rule-base parameters.
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