Title:pClass+: A novel Evolving Semi-supervised ClassifierAuthor(s):Mahardhika Pratama, Edwin Lughofer, Chee-Peng Lim, Wenny Rahayu, Tharam Dillon, Agus BudiyonoAbstract:A novel evolving semi-supervised classifier, namely Parsimonious Classifier+ (pClass+), is proposed in this paper. pClass+ enhances a recently developed classifier, namely pClass, for a semi-supervised learning scenario. As with its predecessor, pClass+ is capable of initiating its learning process from scratch with an empty rule base and adopts an open network structure, where fuzzy rules are evolved, pruned, and recalled automatically on demands. The novelty of pClass+ lies in an online active learning technique, which decreases operator’s annotation efforts and expedites its training process. pClass+ is also equipped with a new parameter identification strategy to cope with the class overlapping situation. The efficacy of pClass+ has been experimentally validated with numerous synthetic and real-world study cases, confirmed by thorough statistical tests and comparisons against state-of-the art classifiers, where pClass+ outperforms its counterparts in achieving the best trade-off between accuracy and complexity.Journal:International Journal of Fuzzy SystemsPublisher:SpringerISSN:2199-3211Page Reference:page 863-880, 18 page(s)Publishing:2017Volume:19Number:3