Evidence Optimization for Consequently Generated Models

Скачать account_balance Ссылка language


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


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


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.

Ключевые слова: Аппроксимация


Контактная информация

location_on  117246, Москва, Научный проезд, д. 17, 15 этаж

phone  +7 (495) 669-68-15

mail_outline  info@datadvance.ru

Связаться navigate_next Реселлеры navigate_next

Подписаться на рассылку