The Semantic Classification Approach Base on Neural Networks

This paper addresses the classification problem, and a semantic classification approach using neural networks is proposed. The approach embeds the theoretical findings of the axiomatic fuzzy set theory in neural networks. Complex concepts are extracted by neural networks, which means that the class...

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Bibliographic Details
Published inIEEE access Vol. 8; pp. 14573 - 14578
Main Authors Shi, Yanli, Zhao, Jinxing
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Artificial neural networks
Classification
Classification algorithms
complex concepts
Fuzzy logic
Fuzzy set theory
Fuzzy sets
Lattices
Neural networks
Portable computers
semantic interpretation
Semantics
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Summary:This paper addresses the classification problem, and a semantic classification approach using neural networks is proposed. The approach embeds the theoretical findings of the axiomatic fuzzy set theory in neural networks. Complex concepts are extracted by neural networks, which means that the class description is formed analytically rather than by tuning parameters of constraint conditions. The experiments are carried out on five benchmark datasets and compared results with five other neural network-based classifiers. The experimental results show that the proposed approach produces high classification accuracy and has a better explanation.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2966227