Evidence optimization for consequently generated models

Evidence optimization for consequently generated models

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Article ID: iaor20125730
Volume: 57
Issue: 1-2
Start Page Number: 50
End Page Number: 56
Publication Date: Jan 2013
Journal: Mathematical and Computer Modelling
Authors: , ,
Keywords: simulation: applications
Abstract:

To construct an adequate regression model one has to fulfill the set of measured features with their generated derivatives. Often the number of these features exceeds the number of the samples in the data set. After a feature generation process the problem of feature selection from a set of highly correlated features arises. The proposed algorithm uses an evidence maximization procedure to select a model as a subset of generated features. During the selection process it rejects multicollinear features. A problem of European option volatility modeling illustrates the algorithm. Its performance is compared with the performances of similar well‐known algorithms.

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