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Dynamic swarm class rebalancing for the process mining of rare events

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Abstract

Process mining is becoming an indispensable method in workflow model reconstructions, offering insights into mission critical systems. The efficacy of process mining depends on whether the underlying data mining algorithms can accurately classify or predict future events from process logs. However, exceptional events are scarce in most operational processes. Hence, the process logs generated from these processes are highly imbalanced. It is quite often that any model learned from imbalanced data tends to be overly generalized toward the normal classes but under-trained to recognize the rare classes. In this paper, we propose 3 methods to rectify this class imbalance problem. They are founded upon a meta-heuristic–swarm intelligence algorithm. The first method, and also the base of the remaining 2 methods, is Dynamic Multi-objective Rebalancing Algorithm, which considers both high accuracy and high confidence level of classification in its objective function, and it is draw upon the particle swarm optimization algorithm. The other two algorithms are hybrid methods by combining the first base method with over-sampling and under-sampling techniques. Experiments are conducted using the three above-mentioned methods to process rebalanced dataset, as well as using other classic resampling methods for comparison. According to the results, our proposed methods show satisfactory performance over other comparison methods, and we extracted meaningful decision rules from a rebalanced dataset in process mining.

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Acknowledgement

The authors are grateful for the financial support from the Research Grants, Nature-Inspired Computing and Metaheuristics Algorithms for Optimizing Data Mining Performance, Grant no. MYRG2016-00069-FST, offered by the University of Macau, FST, and RDAO. The authors appreciate the contribution that Dr. Jingyan Li has made for this paper during his PhD period in the University of Macau, and he is now a data scientist in Huawei Technologies CO. LTD, China.

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Authors and Affiliations

  1. Department of Computer and Information Science, University of Macau, Taipa, Macau SAR, China

    Jinyan Li, Yaoyang Wu & Simon Fong

  2. Zhuhai Institute of Advanced Technology Chinese Academy of Science, Zhuhai, China

    Yaoyang Wu

  3. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia

    Raymond K. Wong

  4. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Victor W. Chu

  5. Business School, La Trobe University, Victoria, Australia

    Kok-leong Ong

  6. School of Medicine, Western Sydney University, Campbell town, NSW, 2560, Australia

    Kelvin K. L. Wong

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  1. Jinyan Li
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Correspondence to Yaoyang Wu.

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Appendix

Appendix

See Tables 2, 3, 4, 5, 6, 7, 8, 9, 10.

Table 2 Performances of Secom dataset with different rebalancing methods
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Table 3 Statistics of the extracted rules from each method's rebalanced datasets
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Table 4 Rules extracted from Secom dataset by SaCb-SDMORA-NB + JRIP
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Table 5 Results of Kappa with different datasets and algorithms
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Table 6 Results of Accuracy with different datasets and algorithms
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Table 7 Results of BER with different datasets and algorithms
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Table 8 Majority class and Minority class Variations of pre-/post-processed by different algorithms in different dataset
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Table 9 Majority class and Minority class Variations of pre-/post-processed by different algorithms in different dataset
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Table 10 Results of %Time with different datasets for the swarm intelligence-based algorithms
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Li, J., Wu, Y., Fong, S. et al. Dynamic swarm class rebalancing for the process mining of rare events. J Supercomput 77, 7549–7583 (2021). https://doi.org/10.1007/s11227-020-03500-x

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