Linear-time One-Class Classification with Repeated Element-wise Folding

TL;DR

This paper introduces Repeated Element-wise Folding (REF), a simple, fast, and robust linear-time method for one-class classification that performs comparably or better than complex existing algorithms.

Contribution

The paper presents REF, a novel linear-time one-class classification algorithm that simplifies hyperparameter tuning and maintains high performance.

Findings

REF achieves comparable or superior accuracy to complex methods.

REF operates with linear time complexity, making it computationally efficient.

Experimental results on benchmark datasets validate the effectiveness of REF.

Abstract

This paper proposes an easy-to-use method for one-class classification: Repeated Element-wise Folding (REF). The algorithm consists of repeatedly standardizing and applying an element-wise folding operation on the one-class training data. Equivalent mappings are performed on unknown test items and the classification prediction is based on the item's distance to the origin of the final distribution. As all the included operations have linear time complexity, the proposed algorithm provides a linear-time alternative for the commonly used computationally much more demanding approaches. Furthermore, REF can avoid the challenges of hyperparameter setting in one-class classification by providing robust default settings. The experiments show that the proposed method can produce similar classification performance or even outperform the more complex algorithms on various benchmark datasets. Matlab codes for REF are publicly available at https://github.com/JenniRaitoharju/REF.