A unified approach for measuring precision and generalization based on anti-alignments

View/Open
Cita com:
hdl:2117/99938
Document typeConference report
Defense date2016
PublisherSpringer
Rights accessOpen Access
Abstract
The holy grail in process mining is an algorithm that, given an event log, produces fitting, precise, properly generalizing and simple process models. While there is consensus on the existence of solid metrics for fitness and simplicity, current metrics for precision and generalization have important flaws, which hamper their applicability in a general setting. In this paper, a novel approach to measure precision and generalization is presented, which relies on the notion of antialignments. An anti-alignment describes highly deviating model traces with respect to observed behavior. We propose metrics for precision and generalization that resemble the leave-one-out cross-validation techniques, where individual traces of the log are removed and the computed anti-alignment assess the model’s capability to describe precisely or generalize the observed behavior. The metrics have been implemented in ProM and tested on several examples.
CitationDongen, B., Carmona, J., Chatain, T. A unified approach for measuring precision and generalization based on anti-alignments. A: International Conference on Business Process Management. "Business Process Management: 14th International Conference, BPM 2016, Rio de Janeiro, Brazil, September 18-22, 2016: proceedings". Rio de Janeiro: Springer, 2016, p. 39-56.
ISBN978-3-319-45348-4
Publisher versionhttp://link.springer.com/chapter/10.1007/978-3-319-45348-4_3
Files | Description | Size | Format | View |
---|---|---|---|---|
Carmona.pdf | 402,6Kb | View/Open |
All rights reserved. This work is protected by the corresponding intellectual and industrial
property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public
communication or transformation of this work are prohibited without permission of the copyright holder