Distributed Processing of Biosignal-Database for Emotion Recognition with Mahout

TL;DR

This paper demonstrates how distributed processing with Mahout on a Cloudera cluster accelerates emotion recognition from biosignals by enabling efficient training of classifiers on large datasets.

Contribution

It introduces a method for applying distributed machine learning to biosignal data for emotion recognition, showcasing the use of Mahout with Cloudera for scalable processing.

Findings

Distributed processing reduces training time significantly.

Enables handling of large physiological datasets.

Improves scalability of emotion recognition models.

Abstract

This paper investigates the use of distributed processing on the problem of emotion recognition from physiological sensors using a popular machine learning library on distributed mode. Specifically, we run a random forests classifier on the biosignal-data, which have been pre-processed to form exclusive groups in an unsupervised fashion, on a Cloudera cluster using Mahout. The use of distributed processing significantly reduces the time required for the offline training of the classifier, enabling processing of large physiological datasets through many iterations.