Experience-consistent modeling: Regression and classification problems |
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Authors: | Witold Pedrycz [Author Vitae] Partab Rai [Author Vitae] |
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Affiliation: | a Department of Electrical & Computer Engineering, University of Alberta, Edmonton, AB T6R 2G7, Canada b System Research Institute, Polish Academy of Sciences, Warsaw, Poland |
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Abstract: | In this study, we are concerned with system modeling which involves limited data and reconciles the developed model with some previously acquired domain knowledge being captured in the format of already constructed models. Each of these previously available models was formed on a basis of extensive data sets which are not available for the current identification pursuits. To emphasize the nature of modeling being guided by the reconciliation mechanisms, we refer to this mode of identification as experience-consistent modeling. The paper presents the conceptual and algorithmic framework by focusing on regression models. By forming a certain extended form of the performance index, it is shown that the domain knowledge captured by regression models can play a similar role as a regularization component used quite commonly in system identification. Experimental results involve both synthetic low-dimensional data and selected data coming from Machine Learning repository. The data used in the experiments tackle regression models as well as classification problems (two-class classifiers). |
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Keywords: | System identification Data Knowledge-based guidance Experience consistency Linear regression Pattern classification Knowledge-based regularization |
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