Structural feature selection for event logs

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Standard

Structural feature selection for event logs. / Hinkka, Markku; Lehto, Teemu; Heljanko, Keijo; Jung, Alexander.

Business Process Management Workshops - BPM 2017 International Workshops, Revised Papers. Springer Verlag, 2018. p. 20-35 (Lecture Notes in Business Information Processing; Vol. 308).

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Hinkka, M, Lehto, T, Heljanko, K & Jung, A 2018, Structural feature selection for event logs. in Business Process Management Workshops - BPM 2017 International Workshops, Revised Papers. Lecture Notes in Business Information Processing, vol. 308, Springer Verlag, pp. 20-35, International Conference on Business Process Management, Sydney, Australia, 09/09/2018. https://doi.org/10.1007/978-3-319-74030-0_2

APA

Hinkka, M., Lehto, T., Heljanko, K., & Jung, A. (2018). Structural feature selection for event logs. In Business Process Management Workshops - BPM 2017 International Workshops, Revised Papers (pp. 20-35). (Lecture Notes in Business Information Processing; Vol. 308). Springer Verlag. https://doi.org/10.1007/978-3-319-74030-0_2

Vancouver

Hinkka M, Lehto T, Heljanko K, Jung A. Structural feature selection for event logs. In Business Process Management Workshops - BPM 2017 International Workshops, Revised Papers. Springer Verlag. 2018. p. 20-35. (Lecture Notes in Business Information Processing). https://doi.org/10.1007/978-3-319-74030-0_2

Author

Hinkka, Markku ; Lehto, Teemu ; Heljanko, Keijo ; Jung, Alexander. / Structural feature selection for event logs. Business Process Management Workshops - BPM 2017 International Workshops, Revised Papers. Springer Verlag, 2018. pp. 20-35 (Lecture Notes in Business Information Processing).

Bibtex - Download

@inproceedings{73a3d16b350445479dd629a9454872c2,
title = "Structural feature selection for event logs",
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.",
keywords = "Automatic business process discovery, Classification, Clustering, Feature selection, Machine learning, Prediction, Process mining",
author = "Markku Hinkka and Teemu Lehto and Keijo Heljanko and Alexander Jung",
year = "2018",
doi = "10.1007/978-3-319-74030-0_2",
language = "English",
isbn = "9783319740294",
series = "Lecture Notes in Business Information Processing",
publisher = "Springer Verlag",
pages = "20--35",
booktitle = "Business Process Management Workshops - BPM 2017 International Workshops, Revised Papers",
address = "Germany",

}

RIS - Download

TY - GEN

T1 - Structural feature selection for event logs

AU - Hinkka, Markku

AU - Lehto, Teemu

AU - Heljanko, Keijo

AU - Jung, Alexander

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - Automatic business process discovery

KW - Classification

KW - Clustering

KW - Feature selection

KW - Machine learning

KW - Prediction

KW - Process mining

UR - http://www.scopus.com/inward/record.url?scp=85041741530&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-74030-0_2

DO - 10.1007/978-3-319-74030-0_2

M3 - Conference contribution

SN - 9783319740294

T3 - Lecture Notes in Business Information Processing

SP - 20

EP - 35

BT - Business Process Management Workshops - BPM 2017 International Workshops, Revised Papers

PB - Springer Verlag

ER -

ID: 17832384