BulDML at Institute of Mathematics and Informatics >
International Book Series Information Science and Computing >
2009 >
Book 08 Classification Forecasting Data Mining >

Please use this identifier to cite or link to this item:

Title: Optimal Decision Rules in Logical Recognition Models
Authors: Gupal, Anatol
Ryazanov, Vladimir
Keywords: Precedent-Recognition Recognition
Logical Regularities of Classes
Estimate Calculation Algorithms
Integer Programming
Decision Rules
Sigmoid Formatting Rules
Issue Date: 2009
Publisher: Institute of Information Theories and Applications FOI ITHEA
Abstract: The task of smooth and stable decision rules construction in logical recognition models is considered. Logical regularities of classes are defined as conjunctions of one-place predicates that determine the membership of features values in an intervals of the real axis. The conjunctions are true on a special no extending subsets of reference objects of some class and are optimal. The standard approach of linear decision rules construction for given sets of logical regularities consists in realization of voting schemes. The weighting coefficients of voting procedures are done as heuristic ones or are as solutions of complex optimization task. The modifications of linear decision rules are proposed that are based on the search of maximal estimations of standard objects for their classes and use approximations of logical regularities by smooth sigmoid functions.
ISSN: 1313-0455
Appears in Collections:Book 08 Classification Forecasting Data Mining

Files in This Item:

File Description SizeFormat
ibs-08-p01.pdf151.23 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.


Valid XHTML 1.0!   Creative Commons License