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A Neural-Like Hierarchical Structure in the Problem of Automatic Generation of Hypotheses of Rules for Classifying the Objects Specified by Sets of Fuzzy Features

  • Konstantin V. Sidorov
  • Natalya N. Filatova
  • Pavel D. Shemaev
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 874)

Abstract

The article describes a toolkit from the class of hybrid neural network models, which integrates a strategy of generalization and methods of fuzzy data processing. Evolution of properties of these models through the tasks of rules hypotheses construction for the purpose of classification of objects specified by fuzzy features can improve an efficiency of interpreters, designed for signals of bioengineering systems and for other data recorded from medical equipment. Considered the possibility of neural-like hierarchical structure (NLHS) utilization for the task of automatic generation of rules hypotheses for classification of objects determined by discrete features. We propose a hybrid algorithm to solve this problem, which acts as generalization generator, creating descriptions close in form to expert’s constructions. The algorithm includes in a composition of classification rules only the most significant features. The apparatus of fuzzy sets can be used for description of feature spaces. This article describes the software which implements the presented algorithm and also demonstrates results of its work with artificial data sets. The Fisher’s irises classification task was used to assess program capabilities. The variants of NLHS-based classification rules, created for a training set, considers as hypotheses, which verification is carried out using a test set and an expert analysis of their structure. Options of rules correction are checked with expert’s support besides the accuracy and completeness of the corrected rules are also evaluated. The constructed NLHS and corresponding training set is saved as a basic archive explaining formalisms contained in interpreter rules.

Keywords

Algorithm Classification Neural-like hierarchical structure Fuzzy set Fuzzy sign Production rule Training set Test set 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Information TechnologiesTver State Technical UniversityTverRussia

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