Authors: Kieran Greer
Addresses: Distributed Computing Systems, Belfast, UK
Abstract: This paper describes a new method for classifying a dataset that partitions elements into their categories. It has relations with neural networks but a slightly different structure, requiring only a single pass through the classifier to generate the weight sets. A grid-like structure is required as part of a novel idea of converting a 1D row of real values into a 2D structure of value bands. Each cell in any band then stores a distinct set of weights, to represent its own importance and its relation to each output category. During classification, all of the output weight lists can be retrieved and summed to produce a probability for what the correct output category is. The bands possibly work like hidden layers of neurons, but they are variable specific, making the process orthogonal. The construction process can be a single update process without iterations, making it potentially much faster. It can also be compared with k-NN and may be practical for partial or competitive updating.
Keywords: single-pass classifiers; categorical data; neural networks; grid architecture; deconstructed data; orthogonal; classification; k-NN; k-nearest neighbour.
International Journal of Computational Systems Engineering, 2017 Vol.3 No.1/2, pp.27 - 34
Available online: 20 Mar 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article