An Integrated ANN-GA Approach to Data Classification
Stanislav Alkhasov, Alexander Tselykh, Alexey Tselykh
Available Online May 2016.
- https://doi.org/10.2991/itsmssm-16.2016.2How to use a DOI?
- classification, artificial neural networks, genetic algorithms, ANN-GA
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - 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 - https://doi.org/10.2991/itsmssm-16.2016.2 ID - Alkhasov2016/05 ER -