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Evidence Optimization for Consequently Generated Models

2010-12-04
Strijov, Vadim
Krymova, Katya
Weber, Gerhard Wilhelm
To construct as 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 modelling illustrates the algorithm. Its performance is compared with performance of similar well-known algorithms.