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An empirical comparison of classification techniques for next event prediction using business process event logs
- Source :
- Expert Systems with Applications. 129:233-245
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- Predictive analytics is an essential capability in business process management to forecast future status and performance of business processes. In this paper, we focus on one particular predictive monitoring task that is solved using classification techniques, i.e. predicting the next event in a case. Several different classifiers have been recently employed in the literature in this task. However, a quantitative benchmark of different classifiers is currently lacking. In this paper, we build such a benchmark by taking into account 20 classifiers from five families, i.e. trees, Bayesian, rule-based, neural and meta classifiers. We employ six real-world process event logs and consider two different sampling approaches, i.e. case and event-based sampling, and three different validation methods in order to acquire a comprehensive evaluation about the classifiers’ performance. According to our benchmark, the classifier most likely to be the overall superior performer is the credal decision tree (C-DT), followed by the other top-4 performers, i.e. random forest, decision tree, dagging ensemble, and nested dichotomies ensemble. We also provide a qualitative discussion of how features of an event log can affect the choice of best classifier.
- Subjects :
- 0209 industrial biotechnology
Empirical comparison
Business process
business.industry
Computer science
Bayesian probability
General Engineering
Decision tree
02 engineering and technology
Predictive analytics
Machine learning
computer.software_genre
Computer Science Applications
Random forest
Business process management
020901 industrial engineering & automation
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
business
Classifier (UML)
computer
Subjects
Details
- ISSN :
- 09574174
- Volume :
- 129
- Database :
- OpenAIRE
- Journal :
- Expert Systems with Applications
- Accession number :
- edsair.doi...........02b76aa68c4d18bebfd5fe7b4440ddf7