TY - GEN
T1 - Dendrite Ellipsoidal Neuron
AU - Arce, Fernando
AU - Zamora, Erik
AU - Sossa, Humberto
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/30
Y1 - 2017/6/30
N2 - A novel and efficient Dendrite Ellipsoidal Neuron based on hyper-ellipsoids is proposed. By using the clustering algorithm k-means++, the method automatically sets an optimum number of dendrites and increases classification performance. The proposed network overcomes the actual Dendrite Morphological Neural Networks due to it changes hyper-boxes by hyper-ellipsoids that create smoother decision boundaries. This technique automatically generates clusters which are converted to hyper-ellipsoids; these hyper-ellipsoids set geometric boundaries and are used to assign patterns to the corresponding classes. The new training method was tested with three synthetic and eight real databases showing superiority over the state-of-the-art for Dendrite Morphological Neural Network training algorithms and a good performance over Multilayer Perceptrons, Support Vector Machines and Radial Basis Function Networks.
AB - A novel and efficient Dendrite Ellipsoidal Neuron based on hyper-ellipsoids is proposed. By using the clustering algorithm k-means++, the method automatically sets an optimum number of dendrites and increases classification performance. The proposed network overcomes the actual Dendrite Morphological Neural Networks due to it changes hyper-boxes by hyper-ellipsoids that create smoother decision boundaries. This technique automatically generates clusters which are converted to hyper-ellipsoids; these hyper-ellipsoids set geometric boundaries and are used to assign patterns to the corresponding classes. The new training method was tested with three synthetic and eight real databases showing superiority over the state-of-the-art for Dendrite Morphological Neural Network training algorithms and a good performance over Multilayer Perceptrons, Support Vector Machines and Radial Basis Function Networks.
UR - http://www.scopus.com/inward/record.url?scp=85030995523&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2017.7965933
DO - 10.1109/IJCNN.2017.7965933
M3 - Contribución a la conferencia
AN - SCOPUS:85030995523
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 795
EP - 802
BT - 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 International Joint Conference on Neural Networks, IJCNN 2017
Y2 - 14 May 2017 through 19 May 2017
ER -