Title:pClass: An Effective Classifier for Streaming ExamplesAuthor(s):Mahardhika Pratama, Sreenatha Anavatti, M.J. Er, Edwin LughoferAbstract: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-base 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’s architectures engaging Multi-Input-Multi-Output (MIMO), Multi-Model (MM) and Round Robin (RR) architectures are also critically analyzed. The efficacy of 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 pClass delivers more superior performance in terms of classification rate, number of fuzzy rules and number of rule-base parametersJournal:IEEE Transactions on Fuzzy SystemsPublisher:IEEE PressISSN:1941-0034Page Reference:page 369-386, 18 page(s)Publishing:2015Volume:23Number:2