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

Titelaufnahme

Titel
An Incremental On-line Classifier for Imbalanced, Incomplete, and Noisy Data
Verfasser
Tscherepanow, Marko ; Riechers, Sören
Herausgeber
Lemaire, Vincent ; Lamirel, Jean-Charles ; Cuxac, Pascal
Erschienen
2012
Sprache
Englisch
Dokumenttyp
Konferenzband
Schlagwörter
TopoART / on-line learning / incremental learning / Adaptive Resonance Theory / classification
URN
urn:nbn:de:0070-pub-25191229

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Abstract

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 topology-learning 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.

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