The article analyzes the specific features in configuring hyperparameters for the Adaboost when it is used to analyze the «significant» level of transitions between stages of a business process grid graph. Therefore, hyperparameter settings have been defined, yielding almost the same result as with the AutoML (“grid search”) for those hyperparameters, that lowering the GPU (CPU) load substantially.
REFERENCES
1.
M.
Panfilov
, I.
Goncharenko
and A.
Bugaenko
, “Application of DS Methods for Solving Applied Task in Finance”
, (AI Journey Conference
, Kaliningrad
, 2019
).2.
A. A.
Bugaenko
, “Application of Machine Learning for Post Process Mining Analysis and Problem Detection in Bank”, in Artificial Intelligence in Intelligent Systems, CSOC 2021
, Lecture Notes in Networks and Systems
, 229
, edited by R.
Silhavy
, et al. (Springer
, Cham
, 2021).3.
R. Sarno F.
Sinaga
and K. R.
Sungkono
, Anomaly Detection in Business Processes Using Process Mining and Fuzzy Association Rule Learning
, Journal of Big Data
7
(1
), 5
(2020
).4.
P.
Tang
, W.
Qiu
, Z.
Huang
, H.
Lian
and Z.
Li
, Anomaly Detection in Electronic Invoice Systems Based on Machine Learning
, Information Sciences
535
, 172
–186
(2020
).5.
F.
Angiulli
, S.
Basta
, S.
Lodi
and C.
Sartori
, Reducing Distance Computations for Distance-Based Outliers, Expert Systems with Applications
147
, 113215
(2020
).6.
C.
Liu
, J.
Yang
and J.
Wu
, Web Intrusion Detection System Combined with Feature Analysis and SVM Optimization
, Eurasip Journal on Wireless Communications and Networking
2020
(1
), 33
(2020
).7.
M.
Pourbafrani
, S. J.
Van Zelst
and W. M. P.
Van Der Aalst
, “Supporting Decisions in Production Line Processes by Combining Process Mining and System Dynamics
”, Advances in Intelligent Systems and Computing 1131 AISC
, pp. 461
–467
, 2020
.8.
E.
Lima
, P.
Davies
, J.
Kaler
, F.
Lovatt
and M.
Green
, Variable Selection for Inferential Models with Relatively High-Dimensional Data: Between Method Heterogeneity and Covariate Stability as Adjuncts to Robust Selection
, Scientific Reports
10
(1
), 8002
(2020
).9.
W. W.
Tso
, B.
Burnak
and E. N.
Pistikopoulos
, bHY-POP: Hyperparameter Optimization of Machine Learning Models Through Parametric Programming
, Computers and Chemical Engineering
139
, 106902
(2020
).10.
J. O.
Berger
, D.
Sun
and C.
Song
, An Objective Prior for Hyperparameters in Normal Hierarchical Models
, Journal of Multivariate Analysis
178
, 104606
(2020
).11.
W. M. P.
Van Der Aalst
, “Process Mining: Discovery, Conformance and Enhancement of Business Processes” (Springer Verlag
, 2011
) ISBN 978-3-642-19344-6.12.
S. J. J.
Leemans
and D.
Fahland
, Information-preserving abstractions of event data in process mining
, Knowledge and Information Systems
62
(3
), 1143
–1197
(2020
).13.
L.
Delcoucq
, F.
Lecron
, P.
Fortemps
and W. M. P.
Van Der Aalst
, “Resource-Centric Process Mining: Clustering Using Local Process Models
”, Proceedings of the ACM Symposium on Applied Computing
, pp. 45
–52
(2020
).
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