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Comuzzi, Marco
Intelligent Enterprise Lab (IEL)
Research Interests
  • business process management, enterprise systems, process monitoring, compliance
  • management, ERP system, business network, design science

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A diagnostic framework for imbalanced classification in business process predictive monitoring

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dc.contributor.author Kim, Jongchan ko
dc.contributor.author Comuzzi, Marco ko
dc.date.available 2021-07-22T08:15:03Z -
dc.date.created 2021-07-21 ko
dc.date.issued 2021-12 ko
dc.identifier.citation EXPERT SYSTEMS WITH APPLICATIONS, v.184, pp.115536 ko
dc.identifier.issn 0957-4174 ko
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/53243 -
dc.description.abstract One of the use cases of business process predictive monitoring is predicting the next activity in a running case, which results in a multi-class classification problem. Approaches to this use case are usually evaluated considering average performance across all classes. This often masks poor performance on minority classes, particularly when classes to be predicted are imbalanced. This is the natural case in next activity prediction, where exceptions or optional activities occur, by design, less frequently than others. In this paper we propose a framework to diagnose poor predictive performance on the minority class in the next activity prediction use case that comprises two tools: an empirical comparison of different resampling techniques in the data preparation phase and a novel classification performance measure. The proposed performance measure aims at highlighting the poor recall on the minority class of a classifier, which is a particularly important performance in the context of next activity prediction, whereas the benchmark helps understanding which resampling technique would be the best at mitigating the poor recall. We also discuss how the two tools of the proposed framework can be combined from an AutoML perspective. The proposed framework has been evaluated on a set of publicly available event logs. ko
dc.language 영어 ko
dc.publisher Pergamon Press Ltd. ko
dc.title A diagnostic framework for imbalanced classification in business process predictive monitoring ko
dc.type ARTICLE ko
dc.type.rims ART ko
dc.identifier.doi 10.1016/j.eswa.2021.115536 ko
dc.identifier.url https://www.sciencedirect.com/science/article/pii/S095741742100943X?via%3Dihub ko
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