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01-09-2010 | Original Paper | Issue 11/2010

Soft Computing 11/2010

A classification and regression technique to handle heterogeneous and imperfect information

Journal:
Soft Computing > Issue 11/2010
Authors:
M. Carmen Garrido, Jose M. Cadenas, Piero P. Bonissone

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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