Argumentation Approach and Learning Methods in Intelligent Decision Support Systems in the Presence of Inconsistent Data | Zendy

Marina Fomina | Zendy; Oleg Morosin | Zendy; В. Н. Вагин | Zendy

Open Access

Argumentation Approach and Learning Methods in Intelligent Decision Support Systems in the Presence of Inconsistent Data

Author(s) -

Marina Fomina,

Oleg Morosin,

В. Н. Вагин

Publication year - 2014

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2014.05.142

Subject(s) - computer science , generalization , argumentation theory , decision support system , intelligent decision support system , artificial intelligence , machine learning , rough set , mathematics , mathematical analysis , philosophy , epistemology

This paper contains a description of methods and algorithms for working with inconsistent data in intelligent decision support systems. An argumentation approach and application of rough sets for generalization problems are considered. The methods for finding the conflicts and the generalization algorithm based on rough sets are proposed. Noise models in the generalization algorithm are viewed. Experimental results are introduced. A decision of some problems that are not solvable in classical logics is given

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