Dendrite Morphological Neural Networks trained by Differential Evolution

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.