Multi-classifier adaptive training: Specialising an activity recognition classifier using semi-supervised learning

Abstract

When an activity recognition classifier is deployed to be used with a particular user, its performance can often be improved by adapting it to that user. To improve the classifier, we propose a novel semisupervised Multi-Classifier Adaptive Training algorithm (MCAT) that uses four classifiers. First, the General classifier is trained on the labelled data available before deployment. Second, the Specific classifier is trained on a limited amount of labelled data specific to the new user in the current environment. Third, a domain-independent meta-classifier decides whether to classify a new instance with the General or Specific classifier. Fourth, another meta-classifier decides whether to include the new instance in the training set for the General classifier. The General classifier is periodically retrained, gradually adapting to the new user in the new environment where it is deployed. The results show that our new algorithm outperforms competing approaches and increases the accuracy of the initial activity recognition classifier by 12.66 percentage points on average.