A Single-Pass Classifier for Categorical Data

TL;DR

This paper introduces a novel single-pass classifier for categorical data that transforms 1-D data into a 2-D grid of weight bands, enabling fast, non-iterative classification with potential advantages over traditional methods.

Contribution

It proposes a new grid-based structure for classification that requires only one pass and no iterative training, differing from neural networks and k-NN.

Findings

Single-pass classification process demonstrated.

Potential for faster training and updating.

Comparable performance to k-NN in certain scenarios.

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 1-D row of real values into a 2-D 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.