Accuracy Improvement of Neural Networks Through Self-Organizing-Maps over Training Datasets
|Gutiérrez Galán, Daniel
Domínguez Morales, Juan Pedro
Tapiador Morales, Ricardo
Ríos Navarro, José Antonio
Domínguez Morales, Manuel Jesús
Jiménez Fernández, Ángel Francisco
Linares Barranco, Alejandro
|Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores
|Although it is not a novel topic, pattern recognition has
become very popular and relevant in the last years. Different classification
systems like neural networks, support vector machines or even
complex statistical ...
Although it is not a novel topic, pattern recognition has become very popular and relevant in the last years. Different classification systems like neural networks, support vector machines or even complex statistical methods have been used for this purpose. Several works have used these systems to classify animal behavior, mainly in an offline way. Their main problem is usually the data pre-processing step, because the better input data are, the higher may be the accuracy of the classification system. In previous papers by the authors an embedded implementation of a neural network was deployed on a portable device that was placed on animals. This approach allows the classification to be done online and in real time. This is one of the aims of the research project MINERVA, which is focused on monitoring wildlife in Do˜nana National Park using low power devices. Many difficulties were faced when pre-processing methods quality needed to be evaluated. In this work, a novel pre-processing evaluation system based on self-organizing maps (SOM) to measure the quality of the neural network training dataset is presented. The paper is focused on a three different horse gaits classification study. Preliminary results show that a better SOM output map matches with the embedded ANN classification hit improvement.
|Gutiérrez Galán, D., Domínguez Morales, J.P., Tapiador Morales, R., Rios Navarro, A., Domínguez Morales, M.J., Jiménez Fernández, Á.F. y Linares Barranco, A. (2017). Accuracy Improvement of Neural Networks Through Self-Organizing-Maps over Training Datasets. En IWANN 2017: 14th International Work-Conference on Artificial Neural Networks (520-531), Cadiz, España: Springer.