Distributed Processing of Biosignal-Database for Emotion Recognition with Mahout

There are many popular emotion definitions and models, both in terms of discrete emotion subsets, as well as mappings in two-and three-dimensional spaces. In this work, we assume a 3D emotional model. With increasing interest in the area, new and large datasets are being collected, enabling new insights to be discovered in the area. These datasets necessitate distributed processing for enhanced scalability and performance. Popular distributed machine learning libraries can augment the process of training accurate classifiers offline, to build prediction models based on large amounts of data. We used Mahout on distributed mode to train a random forest classifier, on the DEAP dataset. Using a distributed approach allowed us to both process the data in reasonable time and conduct many iterations to experiment with different model parameters and convergence criteria.