Classifier ensemble optimization for human activity recognition in smart homes

Recognizing human activities is an active research area due to its applicability in many applications, such as assistive living and healthcare. Currently, the major challenges in activity recognition include the reliability of prediction of each classifier as they differ according to smart homes characteristics. It is not possible that one classifier always performs better than all the other classifiers for every possible situation. Therefore, in this paper, a method for activity recognition is proposed by optimizing the output of multiple classifiers with evolutionary algorithm. We combine the measurement level output of different classifiers in terms of weights for each activity class to make up the ensemble. Classifier ensemble learner generates activity rules by optimizing the prediction accuracy of weighted feature vectors to obtain significant improvement over raw classification. For the evaluation of the proposed method, experiments are performed on two real datasets from CASAS smart home. The results show that our method systematically outperforms single classifier and traditional multiclass models.