Ponencia
Does Your Accurate Process Predictive Monitoring Model Give Reliable Predictions?
Autor/es | Comuzzi, Marco
Márquez Chamorro, Alfonso Eduardo Resinas Arias de Reyna, Manuel |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2018 |
Fecha de depósito | 2022-05-20 |
Publicado en |
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ISBN/ISSN | 978-3-030-17641-9 0302-9743 |
Resumen | The evaluation of business process predictive monitoring
models usually focuses on accuracy of predictions. While accuracy aggre gates performance across a set of process cases, in many practical sce narios decision makers ... The evaluation of business process predictive monitoring models usually focuses on accuracy of predictions. While accuracy aggre gates performance across a set of process cases, in many practical sce narios decision makers are interested in the reliability of an individual prediction, that is, an indication of how likely is a given prediction to be eventually correct. This paper proposes a first definition of business process prediction reliability and shows, through the experimental evalu ation, that metrics that include features defining the variability of a pro cess case often give a better prediction reliability indication than metrics that include the probability estimation computed by the machine learn ing model used to make predictions alone |
Agencias financiadoras | European Union (UE). H2020 Ministerio de Economía y Competitividad (MINECO). España Junta de Andalucía National Research Foundation of Korea (NRF) |
Identificador del proyecto | No. 645751 (RISE BPM)
BELI (TIN2015-70560-R) P12-TIC-1867 NRF-2017076589 |
Cita | Comuzzi, M., Márquez Chamorro, A.E. y Resinas Arias de Reyna, M. (2018). Does Your Accurate Process Predictive Monitoring Model Give Reliable Predictions?. En ICSOC 2018: 16th International Conference on Service-Oriented Computing (367-373), Hangzhou, China: Springer. |
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