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Exploring Interpretability for Predictive Process Analytics
Machine Learning · Submitted 22 Dec 2019
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Findings from this study motivate the need and importance to incorporate interpretability in predictive process analytics.
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The abstract, sentence by sentence
Modern predictive analytics underpinned by machine learning techniques has become a key enabler to the automation of data-driven decision making. In the context of business process management, predictive analytics has been applied to making predictions about the future state of an ongoing business process instance, for example, when will the process instance complete and what will be the outcome upon completion. Machine learning models can be trained on event log data recording historical process execution to build the underlying predictive models. Multiple techniques have been proposed so far which encode the information available in an event log and construct input features required to train a predictive model. While accuracy has been a dominant criterion in the choice of various techniques, they are often applied as a black-box in building predictive models. In this paper, we derive explanations using interpretable machine learning techniques to compare and contrast the suitability of multiple predictive models of high accuracy. The explanations allow us to gain an understanding of the underlying reasons for a prediction and highlight scenarios where accuracy alone may not be sufficient in assessing the suitability of techniques used to encode event log data to features used by a predictive model. Findings from this study motivate the need and importance to incorporate interpretability in predictive process analytics.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Modern predictive analytics underpinned by machine learning techniques has become a key enabler to the automation of data-driven decision making. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | In the context of business process management, predictive analytics has been applied to making predictions about the future state of an ongoing business process instance, for example, when will the process instance complete and what will be the outcome upon completion. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s2 | Machine learning models can be trained on event log data recording historical process execution to build the underlying predictive models. | background 0.92 | 0.06 | 0.02 | asserted 0.94 |
| s3 | Multiple techniques have been proposed so far which encode the information available in an event log and construct input features required to train a predictive model. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s4 | While accuracy has been a dominant criterion in the choice of various techniques, they are often applied as a black-box in building predictive models. | gap 0.70 | 0.05 | 0.02 | asserted 0.93 |
| s5 | In this paper, we derive explanations using interpretable machine learning techniques to compare and contrast the suitability of multiple predictive models of high accuracy. | contribution 0.87 | 0.95 | 0.11 | asserted 0.99 |
| s6 | The explanations allow us to gain an understanding of the underlying reasons for a prediction and highlight scenarios where accuracy alone may not be sufficient in assessing the suitability of techniques used to encode event log data to features used by a predictive model. | interpretation 0.91 | 0.79 | 0.20 | asserted 0.67 |
| s7 | Findings from this study motivate the need and importance to incorporate interpretability in predictive process analytics. | interpretation 0.91 | 0.79 | 0.78 | asserted 0.95 |
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The paper
Original abstract
Modern predictive analytics underpinned by machine learning techniques has become a key enabler to the automation of data-driven decision making. In the context of business process management, predictive analytics has been applied to making predictions about the future state of an ongoing business process instance, for example, when will the process instance complete and what will be the outcome upon completion. Machine learning models can be trained on event log data recording historical process execution to build the underlying predictive models. Multiple techniques have been proposed so far which encode the information available in an event log and construct input features required to train a predictive model. While accuracy has been a dominant criterion in the choice of various techniques, they are often applied as a black-box in building predictive models. In this paper, we derive explanations using interpretable machine learning techniques to compare and contrast the suitability of multiple predictive models of high accuracy. The explanations allow us to gain an understanding of the underlying reasons for a prediction and highlight scenarios where accuracy alone may not be sufficient in assessing the suitability of techniques used to encode event log data to features used by a predictive model. Findings from this study motivate the need and importance to incorporate interpretability in predictive process analytics.
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1 arXiv paper cite this in 2 sentences
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Cites it as background 1 paper
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Sindhgatta et al. [ref] provide both global and local explainability for XGBoost, this is for outcome and remaining time predictions.
XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP · Related Work on Explainable PBPM · 0.86
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