Distributed Processing of Biosignal-Database for Emotion Recognition with Mahout
This work addresses the problem of efficient emotion recognition for researchers by enabling faster processing of large physiological datasets, though it is incremental as it applies existing methods to new data.
The paper tackled emotion recognition from physiological sensors by using distributed processing with Mahout to run a random forests classifier on pre-processed biosignal data, resulting in significantly reduced training time for large datasets.
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.