An Integrated ANN-GA Approach to Data Classification

DOI

10.2991/itsmssm-16.2016.2How to use a DOI?

Keywords

classification, artificial neural networks, genetic algorithms, ANN-GA

Abstract

In this paper, we present an advanced approach to data classification based on the integration of artificial neural networks (ANNs) and genetic algorithms (GAs). We modify neural network architecture in a two-stage process. During the first stage, GA finds a suboptimal neural network architecture: number of nodes, training algorithm, learning rate, etc. Then, the fitting of weight coefficients and bias is carried out in order to minimize GA fitness function. In final section of the paper, we compare the results of the conventional and the proposed approaches.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Stanislav Alkhasov
AU  - Alexander Tselykh
AU  - Alexey Tselykh
PY  - 2016/05
DA  - 2016/05
TI  - An Integrated ANN-GA Approach to Data Classification
BT  - Proceedings of the 2016 Conference on Information Technologies in Science, Management, Social Sphere and Medicine
PB  - Atlantis Press
SP  - 5
EP  - 9
SN  - 2352-538X
UR  - https://doi.org/10.2991/itsmssm-16.2016.2
DO  - 10.2991/itsmssm-16.2016.2
ID  - Alkhasov2016/05
ER  -