An empirical investigation of different classifiers, encoding and ensemble schemes for next event prediction using business process event logs

TL;DR

This paper provides an empirical benchmark for next event prediction in business process logs, analyzing how encoding windows and ensemble schemes affect classifier performance, aiding in optimal method selection.

Contribution

It extends previous benchmarks by evaluating the impact of encoding windows and ensemble schemes on classifier performance for next event prediction.

Findings

Choosing the right number of events for encoding is challenging.

Ensemble schemes improve low-performing classifiers like SVM.

High-performing classifiers like tree-based models are less affected by ensembles.

Abstract

There 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 paper, 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.