Title
Accuracy Improvement of Neural Networks Through Self-Organizing-Maps over Training Datasets.
Abstract
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 Donana 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.
Year
DOI
Venue
2017
10.1007/978-3-319-59153-7_45
ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2017, PT I
Keywords
Field
DocType
Self-organizing map,Artificial neural network,Feedforward neural network,Pattern recognition,Locomotion gaits
Data mining,Feedforward neural network,Evaluation system,Computer science,Support vector machine,Animal behavior,Self-organizing map,Artificial intelligence,Artificial neural network,Machine learning
Conference
Volume
ISSN
Citations 
10305
0302-9743
0
PageRank 
References 
Authors
0.34
4
7