Supervised learning in parallel universes using neighborgrams

Authors:
Bernd Wiswedel;Michael R. Berthold
Affiliations:
Department of Computer and Information Science, University of Konstanz, Germany;Department of Computer and Information Science, University of Konstanz, Germany
Venue:
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Year:
2011

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Abstract

We present a supervised method for Learning in Parallel Universes, i.e. problems given in multiple descriptor spaces. The goal is the construction of local models in individual universes and their fusion to a superior global model that comprises all the available information from the given universes. We employ a predictive clustering approach using Neighborgrams, a one-dimensional data structure for the neighborhood of a single object in a universe. We also present an intuitive visualization, which allows for interactive model construction and visual comparison of cluster neighborhoods across universes.