Dendrite Ellipsoidal Neuron
Fernando Arce, Erik Zamora, Humberto Sossa · 2017
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.