DistAl: an inter-pattern distance-based constructive learning algorithm

Abstract

Multilayer networks of threshold logic units offer an attractive framework for the design of pattern classification systems. A new constructive neural network learning algorithm (DistAl) based on inter-pattern distance is introduced. DistAl constructs a single hidden layer of spherical threshold neurons. Each neuron is designed to exclude a cluster of training patterns belonging to the same class. The weights and thresholds of the hidden neurons are determined directly by comparing the inter-pattern distances of the training patterns. This offers a significant advantage over other constructive learning algorithms that use an iterative weight modification strategy to train individual neurons. The individual clusters are combined by a single output layer of threshold neurons. The speed of DistAl makes it a good candidate for data mining and knowledge acquisition from very large data sets. Results of experiments show that DistAl compares favorably with other neural network learning algorithms for pattern classification.