Classifying the uncertainty arithmetic of individuals using competitive learning neural networks

摘要

The application of artificial neural network technology to a host of problems in pattern recognition has long been advocated. Several analyses comparing the performance of neural networks to the standard methods for achieving machine classification and machine learning, such as statistical pattern recognition and ID3, have been reported. Typically, supervised learning has been used and the specific learning algorithm has been back propagation.For many classification type problems, a priori categories are not available, that is, one does not know explicitly the number of categories existent nor the boundaries delineating these categories. Therefore, known targets with which to train the network are not available. A supervised learning approach is not appropriate under these circumstances; an unsupervised learning algorithm is required.In this article we report on the use of an unsupervised competitive learning algorithm as a classifier. The network was used to classify individuals into categories based on differences in the manner in which individuals manipulate the uncertainty associated with the chaining of rules. The experiment, from which the data to be classified were obtained, is described, results of the neural network approach are compared to classification using a distance measure and to classification using a standard clustering algorithm.