Improving the Classification Performance of Dendrite Morphological Neurons.

Dendrite morphological neurons (DMNs) are neural models for pattern classification, where dendrites are represented by a geometric shape enclosing patterns of the same class. This study evaluates the impact of three dendrite geometries-namely, box, ellipse, and sphere-on pattern classification. In addition, we propose using smooth maximum and minimum functions to reduce the coarseness of decision boundaries generated by typical DMNs, and a softmax layer is attached at the DMN output to provide posterior probabilities from weighted dendrites responses. To adjust the number of dendrites per class automatically, a tuning algorithm based on an incremental-decremental procedure is introduced. The classification performance assessment is conducted on nine synthetic and 49 real-world datasets. Meanwhile, 12 DMN variants are evaluated in terms of accuracy and model complexity. The DMN reaches its highest potential by combining spherical dendrites with smooth activation functions and a learnable softmax layer. It attained the highest accuracy, uses the simplest geometric shape, is insensitive to variables with zero variance, and its structural complexity diminishes by using the smooth maximum function. Furthermore, this DMN configuration performed competitively or even better than other well-established classifiers in terms of accuracy, such as support vector machine, multilayer perceptron, radial basis function network, k -nearest neighbors, and random forest. Thus, the proposed DMN is an attractive alternative for pattern classification in real-world problems.