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There is a growing need for empirical benchmarks that support researchers andpractitioners in selecting the best machine learning technique for givenprediction tasks. In this paper, we consider the next event prediction task inbusiness process predictive monitoring and we extend our previously publishedbenchmark by studying the impact on the performance of different encodingwindows and of using ensemble schemes. The choice of whether to use ensemblesand which scheme to use often depends on the type of data and classificationtask. While there is a general understanding that ensembles perform well inpredictive monitoring of business processes, next event prediction is a taskfor which no other benchmarks involving ensembles are available. The proposedbenchmark helps researchers to select a high performing individual classifieror ensemble scheme given the variability at the case level of the event logunder consideration. Experimental results show that choosing an optimal numberof events for feature encoding is challenging, resulting in the need toconsider each event log individually when selecting an optimal value. Ensembleschemes improve the performance of low performing classifiers in this task,such as SVM, whereas high performing classifiers, such as tree-basedclassifiers, are not better off when ensemble schemes are considered.

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