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Decision Manifolds: Classification Inspired by Self-Organization

Pƶlzlbauer, Georg ; Lidy, Thomas ; Rauber, Andreas

We present a classifier algorithm that approximates the decision surface of labeled data by a patchwork of separating hyperplanes. The hyperplanes are arranged in a way inspired by how Self-Organizing Maps are trained. We take advantage of the fact that the boundaries can often be approximated by linear ones connected by a low-dimensional nonlinear manifold. The resulting classifier allows for a voting scheme that averages over the classifiction results of neighboring hyperplanes. Our algorithm is computationally efficient both in terms of training and classification. Further, we present a model selection framework for estimation of the paratmeters of the classification boundary, and show results for artificial and real-world data sets.

Schlagwörter: Decision Manifolds, supervised learning, ensemble classification
Beteiligte Einrichtung: Technische Fakultät, Arbeitsgruppen der Informatik
DDC-Sachgruppe: Datenverarbeitung, Informatik

Pƶlzlbauer, Georg ; Lidy, Thomas ; Rauber, Andreas  (2007)  Decision Manifolds: Classification Inspired by Self-Organization.

URL: http://biecoll.ub.uni-bielefeld.de/volltexte/2007/131

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 Letzte Änderung: 15.2.2011
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