In this paper, we compare several deep and surface state-of-the-art machine learning methods for risk prediction in problems that can be modelled as a trajectory of events separated by irregular time intervals. Trajectories are the abstract representation of many real-life data, such as patient records, student e-tivities, online financial transactions, and many others. Given the continuously increasing number of machine learning methods to predict future high-risk events in these contexts, we aim to provide more insight into re-producibility and applicability of these methods when changing datasets, parameters, and evaluation measures. As an additional contribution, we release to the community the implementations of all compared methods
A reproducibility study of deep and surface machine learning methods for human-related trajectory prediction / Prenkaj, Bardh; Velardi, Paola; Distante, Damiano; Faralli, Stefano. - (2020). ((Intervento presentato al convegno ACM Conference on Information and Knowledge Management (CIKM) tenutosi a virtual conference.
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Titolo: | A reproducibility study of deep and surface machine learning methods for human-related trajectory prediction | |
Autori: | ||
Data di pubblicazione: | 2020 | |
Citazione: | A reproducibility study of deep and surface machine learning methods for human-related trajectory prediction / Prenkaj, Bardh; Velardi, Paola; Distante, Damiano; Faralli, Stefano. - (2020). ((Intervento presentato al convegno ACM Conference on Information and Knowledge Management (CIKM) tenutosi a virtual conference. | |
Handle: | http://hdl.handle.net/11573/1435344 | |
Appartiene alla tipologia: | 04b Atto di convegno in volume |