A framework to evaluate and compare decision-mining techniques
| Autor: | Jouck, Toon, de Leoni, Massimiliano, Depaire, Benoît, Daniel, Florian, Sheng, Quan Z., Motahari, Hamid |
|---|---|
| Přispěvatelé: | Process Science |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Process modeling
Information Systems and Management Process (engineering) Computer science media_common.quotation_subject Decision mining 02 engineering and technology Machine learning computer.software_genre Evaluation Log generation Management Information Systems Control and Systems Engineering Business and International Management Information Systems Modeling and Simulation 0202 electrical engineering electronic engineering information engineering Quality (business) media_common 060201 languages & linguistics Event (computing) business.industry 06 humanities and the arts 0602 languages and literature 020201 artificial intelligence & image processing Artificial intelligence Decision table business computer Decision model |
| Zdroj: | Business Process Management Workshops ISBN: 9783030116408 Business Process Management Workshops Business Process Management Workshops-BPM 2018 International Workshops, Revised Papers, 482-493 STARTPAGE=482;ENDPAGE=493;TITLE=Business Process Management Workshops-BPM 2018 International Workshops, Revised Papers |
| ISSN: | 1865-1348 |
| DOI: | 10.1007/978-3-030-11641-5_38 |
| Popis: | During the last decade several decision mining techniques have been developed to discover the decision perspective of a process from an event log. The increasing number of decision mining techniques raises the importance of evaluating the quality of the discovered decision models and/or decision logic. Currently, the evaluations are limited because of the small amount of available event logs with decision information. To alleviate this limitation, this paper introduces the ‘DataExtend’ technique that allows evaluating and comparing decision-mining techniques with each other, using a sufficient number of event logs and process models to generate evaluation results that are statistically significant. This paper also reports on an initial evaluation using ‘DataExtend’ that involves two techniques to discover decisions, whose results illustrate that the approach can serve the purpose. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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