RobustSPAM for Inference from Noisy Longitudinal Data and Preservation of Privacy

Anna Palczewska, Jan Palczewski, Georgios Aivaliotis, Lukasz Kowalik
2017 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)  
The availability of complex temporal datasets in social, health and consumer contexts has driven the development of pattern mining techniques that enable the use of classical machine learning tools for model building. In this work we introduce a robust temporal pattern mining framework for finding predictive patterns in complex timestamped multivariate and noisy data. We design an algorithm RobustSPAM that enables mining of temporal patterns from data with noisy timestamps. We apply our
more » ... m to social care data from a local government body and investigate how the efficiency and accuracy of the method depends on the level of noise. We further explore the trade-off between the loss of predictivity due to perturbation of timestamps and the risk of person re-identification.
doi:10.1109/icmla.2017.0-137 dblp:conf/icmla/PalczewskaPAK17 fatcat:teuveqopsjgkza73t654l7edke