Publication
Title
Robust process discovery with artificial negative events
Author
Abstract
Process discovery is the automated construction of structured process models from information system event logs. Such event logs often contain positive examples only. Without negative examples, it is a challenge to strike the right balance between recall and specificity, and to deal with problems such as expressiveness, noise, incomplete event logs, or the inclusion of prior knowledge. In this paper, we present a configurable technique that deals with these challenges by representing process discovery as a multi-relational classification problem on event logs supplemented with Artificially Generated Negative Events (AGNEs). This problem formulation allows using learning algorithms and evaluation techniques that are well-know in the machine learning community. Moreover, it allows users to have a declarative control over the inductive bias and language bias.
Language
English
Source (journal)
Journal of machine learning research. - Cambridge, Mass.
Publication
Cambridge, Mass. : 2009
ISSN
1532-4435
Volume/pages
10:6(2009), p. 1305-1340
ISI
000270824900004
Full text (open access)
UAntwerpen
Faculty/Department
Publication type
Subject
External links
Web of Science
Record
Identification
Creation 12.09.2011
Last edited 17.07.2017