BROWSE

Related Scientist

adhitama,bayu's photo.

adhitama,bayu
데이터사이언스그룹
more info

ITEM VIEW & DOWNLOAD

An Empirical Investigation of Different Classifiers, Encoding, and Ensemble Schemes for Next Event Prediction Using Business Process Event Logs

DC Field Value Language
dc.contributor.authorBayu Adhi Tama-
dc.contributor.authorComuzzi, M.-
dc.contributor.authorKo, J.-
dc.date.accessioned2021-01-21T02:30:04Z-
dc.date.accessioned2021-01-21T02:30:04Z-
dc.date.available2021-01-21T02:30:04Z-
dc.date.available2021-01-21T02:30:04Z-
dc.date.created2020-12-03-
dc.date.issued2020-11-
dc.identifier.issn2157-6904-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/9081-
dc.description.abstractThere is a growing need for empirical benchmarks that support researchers and practitioners in selecting the best machine learning technique for given prediction tasks. In this article, we consider the next event prediction task in business process predictive monitoring, and we extend our previously published benchmark by studying the impact on the performance of different encoding windows and of using ensemble schemes. The choice of whether to use ensembles and which scheme to use often depends on the type of data and classification task. While there is a general understanding that ensembles perform well in predictive monitoring of business processes, next event prediction is a task for which no other benchmarks involving ensembles are available. The proposed benchmark helps researchers to select a high-performing individual classifier or ensemble scheme given the variability at the case level of the event log under consideration. Experimental results show that choosing an optimal number of events for feature encoding is challenging, resulting in the need to consider each event log individually when selecting an optimal value. Ensemble schemes improve the performance of low-performing classifiers in this task, such as SVM, whereas high-performing classifiers, such as tree-based classifiers, are not better off when ensemble schemes are considered-
dc.language영어-
dc.publisherAssociation for Computing Machinery (ACM)-
dc.titleAn Empirical Investigation of Different Classifiers, Encoding, and Ensemble Schemes for Next Event Prediction Using Business Process Event Logs-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.wosid000589194300005-
dc.identifier.scopusid2-s2.0-85095864630-
dc.identifier.rimsid73838-
dc.contributor.affiliatedAuthorBayu Adhi Tama-
dc.identifier.doi10.1145/3406541-
dc.identifier.bibliographicCitationACM Transactions on Intelligent Systems and Technology, v.11, no.6, pp.1 - 34-
dc.relation.isPartOfACM Transactions on Intelligent Systems and Technology-
dc.citation.titleACM Transactions on Intelligent Systems and Technology-
dc.citation.volume11-
dc.citation.number6-
dc.citation.startPage1-
dc.citation.endPage34-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.subject.keywordAuthorbusiness process-
dc.subject.keywordAuthorClassifier ensembles-
dc.subject.keywordAuthorempirical benchmark-
dc.subject.keywordAuthorhomogeneous ensembles-
dc.subject.keywordAuthorindividual classifier-
dc.subject.keywordAuthornext event prediction-
dc.subject.keywordAuthorpredictive monitoring-
Appears in Collections:
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > Data Science Group(데이터 사이언스 그룹) > 1. Journal Papers (저널논문)
Files in This Item:
There are no files associated with this item.

qrcode

  • facebook

    twitter

  • Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
해당 아이템을 이메일로 공유하기 원하시면 인증을 거치시기 바랍니다.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse