TY - GEN
T1 - Dendrite Morphological Neural Networks trained by Differential Evolution
AU - Arce, Fernando
AU - Zamora, Erik
AU - Sossa, Humberto
AU - Barron, Ricardo
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/2/9
Y1 - 2017/2/9
N2 - A new efficient training algorithm for a Dendrite Morphological Neural Network is proposed. Based on Differential Evolution, the method optimizes the number of dendrites and increases classification performance. This technique has two initialisation ways of learning parameters. The first selects all the patterns and opens a hyper-box per class with a length such that all the patterns of each class remain inside. The second generates clusters for each class by k-means++. After the initialisation, the algorithm divides each hyper-box and applies Differential Evolution to the resultant hyper-boxes to place them in the best position and the best size. Finally, the method selects the set of hyper-boxes that produced the least error from the least number. The new training method was tested with three synthetic and six real databases showing superiority over the state-of-the-art for Dendrite Morphological Neural Network training algorithms and a similar performance as well as a Multilayer Perceptron, a Support Vector Machine and a Radial Basis Network.
AB - A new efficient training algorithm for a Dendrite Morphological Neural Network is proposed. Based on Differential Evolution, the method optimizes the number of dendrites and increases classification performance. This technique has two initialisation ways of learning parameters. The first selects all the patterns and opens a hyper-box per class with a length such that all the patterns of each class remain inside. The second generates clusters for each class by k-means++. After the initialisation, the algorithm divides each hyper-box and applies Differential Evolution to the resultant hyper-boxes to place them in the best position and the best size. Finally, the method selects the set of hyper-boxes that produced the least error from the least number. The new training method was tested with three synthetic and six real databases showing superiority over the state-of-the-art for Dendrite Morphological Neural Network training algorithms and a similar performance as well as a Multilayer Perceptron, a Support Vector Machine and a Radial Basis Network.
UR - http://www.scopus.com/inward/record.url?scp=85016075751&partnerID=8YFLogxK
U2 - 10.1109/SSCI.2016.7850259
DO - 10.1109/SSCI.2016.7850259
M3 - Contribución a la conferencia
AN - SCOPUS:85016075751
T3 - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
BT - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Y2 - 6 December 2016 through 9 December 2016
ER -