Evidence Optimization for Consequently Generated Models

Download account_balance Link language

Authors:

Strijov V., Krymova E., Weber G.W.

Journal:

Mathematical and Computer Modelling. 2013. V. 57. P. 50-56.

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.

Keywords: Approximation

LinkedIn
VK

Contact Information

location_on  31100, Toulouse, Avenue du Général de Croutte 42

phone  +33 (0) 5 82-95-59-68

mail_outline  info@datadvance.net

Contact us navigate_next