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Soft Computing

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01.09.2010 | Original Paper

A classification and regression technique to handle heterogeneous and imperfect information

verfasst von: M. Carmen Garrido, Jose M. Cadenas, Piero P. Bonissone

Erschienen in: Soft Computing | Ausgabe 11/2010

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Abstract

Imperfect information inevitably appears in real situations for a variety of reasons. Although efforts have been made to incorporate imperfect data into learning and inference methods, there are many limitations as to the type of data, uncertainty and imprecision that can be handled. In this paper, we propose a classification and regression technique to handle imperfect information. We incorporate the handling of imperfect information into both the learning phase, by building the model that represents the situation under examination, and the inference phase, by using such a model. The model obtained is global and is described by a Gaussian mixture. To show the efficiency of the proposed technique, we perform a comparative study with a broad baseline of techniques available in literature tested with several data sets.

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Titel: A classification and regression technique to handle heterogeneous and imperfect information
verfasst von: M. Carmen Garrido
Jose M. Cadenas
Piero P. Bonissone
Publikationsdatum: 01.09.2010
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 11/2010
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-009-0509-y

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