Evolving Gaussian Mixture Models for Classification

The combination of Gaussian Mixture Models and the Expectation Maximisation algorithm is a powerful tool for clustering tasks. Although there are extensions for the classification task, the success of the approaches is limited, in part because of instabilities in the initialisation method, as it requires a large number of statistical tests. To circumvent this, we propose an 'evolutionary Gaussian Mixture Model' for classification, where a statistical sample of models evolves to a stable solution. Experiments in the domain of Human Activity Recognition are conducted to demonstrate the sensibility of the proposed technique and compare the performance to SVM-based or LSTM-based approaches.