An Incremental On-line Classifier for Imbalanced, Incomplete, and Noisy Data

Incremental on-line learning is a research topic gaining increasing interest in the machine learning community. Such learning methods are highly adaptive, not restricted to distinct training and application phases, and applicable to large volumes of data. In this paper, we present a novel classifier based on the unsupervised topologylearning TopoART neural network. We demonstrate that this classifier is capable of fast incremental on-line learning and achieves excellent results on standard datasets. We further show that it can successfully process imbalanced, incomplete, and noisy data. Due to these properties, we consider it a promising component for constructing artificial agents operating in real-world environments.