RAZOR: Refining Accuracy by Zeroing Out Redundancies

TL;DR

RAZOR is a new instance selection method that efficiently reduces data redundancy, improving learning efficiency without sacrificing accuracy, applicable in both supervised and unsupervised contexts.

Contribution

It introduces RAZOR, a scalable and robust instance selection technique that outperforms existing methods in effectiveness and efficiency for large-scale datasets.

Findings

RAZOR significantly reduces dataset size while maintaining accuracy.

It outperforms recent state-of-the-art techniques in effectiveness.

RAZOR is applicable in both supervised and unsupervised settings.

Abstract

In many application domains, the proliferation of sensors and devices is generating vast volumes of data, imposing significant pressure on existing data analysis and data mining techniques. Nevertheless, an increase in data volume does not inherently imply an increase in informational content, as a substantial portion may be redundant or represent noise. This challenge is particularly evident in the deep learning domain, where the utility of additional data is contingent on its informativeness. In the absence of such, larger datasets merely exacerbate the computational cost and complexity of the learning process. To address these challenges, we propose RAZOR, a novel instance selection technique designed to extract a significantly smaller yet sufficiently informative subset from a larger set of instances without compromising the learning process. RAZOR has been specifically engineered to be robust, efficient, and scalable, making it suitable for large-scale datasets. Unlike many techniques in the literature, RAZOR is capable of operating in both supervised and unsupervised settings. Experimental results demonstrate that RAZOR outperforms recent state-of-the-art techniques in terms of both effectiveness and efficiency.