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Collaborative multi-step mono-level multi-strategy classification

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Abstract

This article deals with the description of a new way to learn from multiple and heterogeneous data sets, and with the integration of this method in a multi-agent hybrid learning system. This system integrates different kinds of unsupervised classification methods and gives a set of clusterings as the result and a unifying result, representing all the other one. In this new approach, the method occurrences compare their results and automatically refine them to try to make them converge towards a unique clustering that unifies all the results. Thus, the data are not really merged but the results from their classification are compared and refined according to the results from all the other data sets. This enables to produce a set of classification hierarchies which classes are very similar, although these hierarchies were extracted from different data sets. Then it is easy to build a unifying result from all of them.

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Authors and Affiliations

  1. LSIIT–AFD, UMR 7005 CNRS/ULP, University Louis Pasteur, Strasbourg, France

    Pierre Gançarski & Cédric Wemmert

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  1. Pierre Gançarski
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  2. Cédric Wemmert
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Correspondence to Pierre Gançarski.

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Gançarski, P., Wemmert, C. Collaborative multi-step mono-level multi-strategy classification. Multimed Tools Appl 35, 1–27 (2007). https://doi.org/10.1007/s11042-007-0115-x

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