Structural Feature Selection for Event Logs

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Hinkka , M , Lehto , T , Heljanko , K & Jung , A 2018 , Structural Feature Selection for Event Logs . in E Teniente & M Weidlich (eds) , BUSINESS PROCESS MANAGEMENT WORKSHOPS (BPM 2017) . Lecture Notes in Business Information Processing , vol. 308 , Springer-Verlag , pp. 20-35 , 15th International Conference on Business Process Management (BPM) , Barcelona , Spain , 10/09/2017 .

Title: Structural Feature Selection for Event Logs
Author: Hinkka, Markku; Lehto, Teemu; Heljanko, Keijo; Jung, Alexander
Other contributor: Teniente, E
Weidlich, M
Contributor organization: Helsinki Institute for Information Technology
Publisher: Springer-Verlag
Date: 2018
Language: eng
Number of pages: 16
Belongs to series: Lecture Notes in Business Information Processing
ISBN: 978-3-319-74029-4
ISSN: 1865-1348
Abstract: We consider the problem of classifying business process instances based on structural features derived from event logs. The main motivation is to provide machine learning based techniques with quick response times for interactive computer assisted root cause analysis. In particular, we create structural features from process mining such as activity and transition occurrence counts, and ordering of activities to be evaluated as potential features for classification. We show that adding such structural features increases the amount of information thus potentially increasing classification accuracy. However, there is an inherent trade-off as using too many features leads to too long run-times for machine learning classification models. One way to improve the machine learning algorithms' run-time is to only select a small number of features by a feature selection algorithm. However, the run-time required by the feature selection algorithm must also be taken into account. Also, the classification accuracy should not suffer too much from the feature selection. The main contributions of this paper are as follows: First, we propose and compare six different feature selection algorithms by means of an experimental setup comparing their classification accuracy and achievable response times. Second, we discuss the potential use of feature selection results for computer assisted root cause analysis as well as the properties of different types of structural features in the context of feature selection.
Subject: Automatic business process discovery
Process mining Prediction
Machine learning
Clustering Feature selection
113 Computer and information sciences
Peer reviewed: Yes
Rights: other
Usage restriction: openAccess
Self-archived version: acceptedVersion

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