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Structural Feature Selection for Event Logs

  • Markku HinkkaEmail author
  • Teemu Lehto
  • Keijo Heljanko
  • Alexander Jung
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 308)

Abstract

We consider the problem of classifying business process instances based on structural features derived from event logs. The main motivation is to provide machine learning based techniques with quick response times for interactive computer assisted root cause analysis. In particular, we create structural features from process mining such as activity and transition occurrence counts, and ordering of activities to be evaluated as potential features for classification. We show that adding such structural features increases the amount of information thus potentially increasing classification accuracy. However, there is an inherent trade-off as using too many features leads to too long run-times for machine learning classification models. One way to improve the machine learning algorithms’ run-time is to only select a small number of features by a feature selection algorithm. However, the run-time required by the feature selection algorithm must also be taken into account. Also, the classification accuracy should not suffer too much from the feature selection. The main contributions of this paper are as follows: First, we propose and compare six different feature selection algorithms by means of an experimental setup comparing their classification accuracy and achievable response times. Second, we discuss the potential use of feature selection results for computer assisted root cause analysis as well as the properties of different types of structural features in the context of feature selection.

Keywords

Automatic business process discovery Process mining Prediction Classification Machine learning Clustering Feature selection 

Notes

Acknowledgements

We want to thank QPR Software Plc for funding our research. Financial support of Academy of Finland projects 139402 and 277522 is acknowledged.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Markku Hinkka
    • 1
    • 2
    Email author
  • Teemu Lehto
    • 1
    • 2
  • Keijo Heljanko
    • 1
    • 3
  • Alexander Jung
    • 1
  1. 1.Department of Computer Science, School of ScienceAalto UniversityEspooFinland
  2. 2.QPR Software PlcHelsinkiFinland
  3. 3.HIIT Helsinki Institute for Information TechnologyEspooFinland

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