论文信息 - THE SEPARABILITY OF SPLIT VALUE CRITERION

THE SEPARABILITY OF SPLIT VALUE CRITERION

The Separability of Split Value (SSV) criterion is a simple and efficient tool for building classification trees and extraction of logical rules. It deals with both continuous and discrete features describing data vectors and requires no user interaction in the learning process. Extensions of methods based on this criterion are presented. They aim at improvement of reliability and efficiency of the methods and extension of their applications area. Good results for several benchmark datasets were obtained.

Wodzisaw Duch | Krzysztof Gr | K. Gr | W. Duch

[1] Kevin Baker,et al. Classification of radar returns from the ionosphere using neural networks , 1989 .

[2] Joachim Diederich,et al. Survey and critique of techniques for extracting rules from trained artificial neural networks , 1995, Knowl. Based Syst..

[3] Catherine Blake,et al. UCI Repository of machine learning databases , 1998 .

[4] David J. Spiegelhalter,et al. Machine Learning, Neural and Statistical Classification , 2009 .

[5] Włodzisław Duch. A framework for similarity-based classification methods , 2000 .

[6] K. Gr,et al. A general purpose separability criterion for classification systems , 1999 .

[7] Wlodzislaw Duch,et al. A new methodology of extraction, optimization and application of crisp and fuzzy logical rules , 2001, IEEE Trans. Neural Networks.

[8] Wlodzislaw Duch,et al. Feature space mapping as a universal adaptive system , 1995 .