Training a HyperDimensional Computing Classifier using a Threshold on its Confidence

TL;DR

This paper introduces a confidence-based training extension for Hyperdimensional Computing classifiers, improving accuracy and confidence levels by incorporating a threshold for correctly classified samples.

Contribution

It proposes a novel training method that considers confidence levels in HDC, enhancing classification accuracy and confidence without increasing computational complexity.

Findings

Performance improved across multiple datasets

Classifier achieves higher confidence in predictions

Method consistently outperforms baseline

Abstract

Hyperdimensional computing (HDC) has become popular for light-weight and energy-efficient machine learning, suitable for wearable Internet-of-Things (IoT) devices and near-sensor or on-device processing. HDC is computationally less complex than traditional deep learning algorithms and achieves moderate to good classification performance. This article proposes to extend the training procedure in HDC by taking into account not only wrongly classified samples, but also samples that are correctly classified by the HDC model but with low confidence. As such, a confidence threshold is introduced that can be tuned for each dataset to achieve the best classification accuracy. The proposed training procedure is tested on UCIHAR, CTG, ISOLET and HAND dataset for which the performance consistently improves compared to the baseline across a range of confidence threshold values. The extended training procedure also results in a shift towards higher confidence values of the correctly classified samples making the classifier not only more accurate but also more confident about its predictions.