An empirical comparison of classification techniques for next event prediction using business process event logs. (1st September 2019)

• Record Type: Journal Article
• Authors: Tama, Bayu Adhi; Comuzzi, Marco
• Is Part Of: Expert systems with applications. Volume 129(2019)
• Page Start: 233
• Page End: 245
• Publication Date: 2019-09-01
• Subjects: Process indicators -- Classification algorithms -- Significance test -- Performance evaluation -- Event log -- Empirical benchmark; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.04.016
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Quantitative benchmark of classifiers in business process predictive monitoring. Considers 20 classifiers from 5 families, with different sampling and validation. Tested on real world data sets. Credal tree emerges as most likely superior performer. 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.