Effective Utilization of Supervised Learning Techniques for Process Model Matching

Authors

  • Khurram Shahzad Punjab University College of Information Technology, University of the Punjab, Lahore
  • Arslaan Mazhar Punjab University College of Information Technology, University of the Punjab, Lahore
  • Ghulam Mustafa University of the Punjab, Gujranwala Campus
  • Faisal Aslam Punjab University College of Information Technology, University of the Punjab, Lahore

Keywords:

Business process management, process model matching, artificial intelligence, supervised learning techniques, machine learning, data balancing

Abstract

The recent attempts to use supervised learning techniques for process model matching have yielded below par performance. To address this issue, we have transformed the well-known benchmark correspondences to a readily usable format for supervised learning. Furthermore, we have performed several experiments using eight supervised learning techniques to establish that imbalance in the datasets is the key reason for the abysmal performance. Finally, we have used four data balancing techniques to generate balanced training dataset and verify our solution by repeating the experiments for the four datasets, including the three benchmark datasets. The results show that the proposed approach increases the matching performance significantly.

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Published

2020-12-16

How to Cite

Shahzad, K., Mazhar, A., Mustafa, G., & Aslam, F. (2020). Effective Utilization of Supervised Learning Techniques for Process Model Matching. COMPUTING AND INFORMATICS, 39(3), 361–384. Retrieved from http://www.cai.sk/ojs/index.php/cai/article/view/2020_3_361

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