Attribute Selection Based on FRiS-Compactness
Nikolay Zagoruiko, Irina Borisova, Vladimir Dyubanov and Olga Kutnenko;
JMLR W&P 10:35-44, 2010.
Abstract
Commonly to classify new object in Data Mining one should estimate its similarity with given classes. Function of Rival Similarity (FRiS) is assigned to calculate quantitative measure of similarity considering a competitive situation. FRiS-function allows constructing new effective algorithms for various Data Mining tasks solving. In particular, it enables to obtain quantitative estimation of compactness of patterns which can be used as indirect criterion for informative attributes selection. FRiS-compactness predicts reliability of recognition of control sample more precisely, than such widespread methods as One-Leave-Out and Cross-Validation. Presented in the paper results of real genetic task solving confirm efficiency of FRiS-function using in attributes selection and decision rules construction.
