An incremental classifier from data streams

Abstract

A novel evolving fuzzy rule-based classifier, namely parsimonious classifier (pClass), is proposed in this paper. pClass can set off its learning process either from scratch with an empty rule base or from an initially trained fuzzy model. Importantly, pClass not only adopts the open structure concept, where an automatic knowledge building process can be cultivated during the training process, which is well-known as a main pillar to learn from streaming examples, but also incorporates the so-called plug-and-play principle, where all learning modules are coupled in the training process, in order to diminish the requirement of pre- or post-processing steps, undermining the firm logic of the online classifier. In what follows, pClass is equipped with the rule growing, pruning, recall and input weighting techniques, which are fully performed on the fly in the training process. The viability of pClass has been tested exploiting real-world and synthetic data streams containing some sorts of concept drifts, and compared with state-of-the-art classifiers, where pClass can deliver the most encouraging numerical results in terms of the classification rate, number of fuzzy rule, number of rule base parameters and the runtime. © 2014 Springer International Publishing.