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E. Ziegel (2003)
The Elements of Statistical LearningTechnometrics, 45
Ario Santoso (2018)
Specification-driven multi-perspective predictive business process monitoringEnterprise
J. Ross Quinlan (1993)
C4Morgan Kaufmann
Niek Tax, I. Verenich, M. Rosa, M. Dumas (2016)
Predictive Business Process Monitoring with LSTM Neural Networks
Bayu Tama, M. Comuzzi (2019)
An empirical comparison of classification techniques for next event prediction using business process event logsExpert Syst. Appl., 129
Bayu Tama, K. Rhee (2017)
A Detailed Analysis of Classifier Ensembles for Intrusion Detection in Wireless NetworkJ. Inf. Process. Syst., 13
Chih-Chung Chang, Chih-Jen Lin (2011)
LIBSVM: A library for support vector machinesACM Trans. Intell. Syst. Technol., 2
Chiara Di Francescomarino, Chiara Ghidini, Fabrizio Maria Maggi, Fredrik Milani (2018)
Predictive process monitoring methods: Which one suits me best? In Proceedings of the International Conference on Business Process ManagementSpringer
D. Wolpert, W. Macready (1997)
No free lunch theorems for optimizationIEEE Trans. Evol. Comput., 1
T. Hastie, R. Tibshirani, J. Friedman (2001)
The Elements of Statistical Learning
Ario Santoso (2018)
Specification-Driven Multi-Perspective Predictive Business Process Monitoring (Extended Version)
Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin (2008)
LIBLINEAR: A Library for Large Linear ClassificationJ. Mach. Learn. Res., 9
R. Duda, P. Hart, D. Stork (2000)
Pattern classification, 2nd Edition
(1985)
Bayesian networks: A model of self-activated memory for evidential reasoning
F. Maggi, Chiara Francescomarino, M. Dumas, Chiara Ghidini (2013)
Predictive Monitoring of Business ProcessesArXiv, abs/1312.4874
J. Quinlan (1992)
C4.5: Programs for Machine Learning
S. Bagui (2005)
Combining Pattern Classifiers: Methods and AlgorithmsTechnometrics, 47
N. Altman (1992)
An Introduction to Kernel and Nearest-Neighbor Nonparametric RegressionThe American Statistician, 46
G. Wang, Jin-Xing Hao, Jian Ma, Hongbing Jiang (2011)
A comparative assessment of ensemble learning for credit scoringExpert Syst. Appl., 38
T. Ho (1998)
The Random Subspace Method for Constructing Decision ForestsIEEE Trans. Pattern Anal. Mach. Intell., 20
M. Friedman (1940)
A Comparison of Alternative Tests of Significance for the Problem of $m$ RankingsAnnals of Mathematical Statistics, 11
Chiara Francescomarino, Chiara Ghidini, F. Maggi, Fredrik Milani (2018)
Predictive Process Monitoring Methods: Which One Suits Me Best?
M. Leoni, Wil Aalst, M. Dees (2016)
A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logsInf. Syst., 56
Mirko Polato, A. Sperduti, Andrea Burattin, M. Leoni (2016)
Time and activity sequence prediction of business process instancesComputing, 100
Joerg Evermann, Jana-Rebecca Rehse, Peter Fettke (2016)
Predicting process behaviour using deep learningDecis. Support Syst., 100
Dominic Breuker, Patrick Delfmann, Martin Matzner, J. Becker (2014)
Designing and Evaluating an Interpretable Predictive Modeling Technique for Business Processes
Bayu Tama, K. Rhee (2017)
An extensive empirical evaluation of classifier ensembles for intrusion detection taskComput. Syst. Sci. Eng., 32
K. Ting, I. Witten (1997)
Stacking Bagged and Dagged Models
J. Abellán, S. Moral (2003)
Building classification trees using the total uncertainty criterionInternational Journal of Intelligent Systems, 18
Bayu Tama, K. Rhee (2019)
Tree-based classifier ensembles for early detection method of diabetes: an exploratory studyArtificial Intelligence Review, 51
P. Leitner, Johannes Ferner, W. Hummer, S. Dustdar (2013)
Data-driven and automated prediction of service level agreement violations in service compositionsDistributed and Parallel Databases, 31
Chuanyi Li, Jidong Ge, LiGuo Huang, Haiyang Hu, Budan Wu, Hongji Yang, Hao Hu, B. Luo (2016)
Process mining with token carried dataInf. Sci., 328
This event log records events from an information system managing road traffic fines for the local police of a city in Italy
S. Hochreiter, J. Schmidhuber (1997)
Long Short-Term MemoryNeural Computation, 9
John Sinclair, Herbert Wiegand, Richard Allsopp, James Arthurs, Arthur Bronstein, Louise Dagenais, Robert London, Christian Kay, Johan Leiden, Franciscus Junius, B. Kipfer, Alan Kirkness, A. Cowie, Ton Broeders, Phil Hyams, Thomas Creamer, Takoma Park, N. Kharma, Hans-Peder Kromann, Theis Riiber, Poul Rosbach, E. Lovatt, J. Mdee, H. Niedzielski, Viggo Pedersen, Sandra Thompson (1992)
TABLE OF CONTENTS Preface
(2020)
This classification technique takes into account the conditional probabilities of a categorical
M. Castellanos, Norman Salazar, F. Casati, U. Dayal, M. Shan (2005)
Predictive business operations management
Average Friedman rank 2
Thomas Baier, Claudio Ciccio, J. Mendling, M. Weske (2015)
Matching of Events and Activities - An Approach Using Declarative Modeling Constraints
M. Hall, E. Frank (2008)
Combining Naive Bayes and Decision Tables
Irene Teinemaa, M. Dumas, M. Rosa, F. Maggi (2017)
Outcome-Oriented Predictive Process MonitoringACM Transactions on Knowledge Discovery from Data (TKDD), 13
Wayne Iba, P. Langley (1992)
Induction of One-Level Decision Trees
R. Holte (1993)
Very Simple Classification Rules Perform Well on Most Commonly Used DatasetsMachine Learning, 11
I. Verenich, M. Dumas, M. Rosa, F. Maggi, Irene Teinemaa (2018)
Survey and Cross-benchmark Comparison of Remaining Time Prediction Methods in Business Process MonitoringACM Transactions on Intelligent Systems and Technology (TIST), 10
M. Friedman (1937)
The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of VarianceJournal of the American Statistical Association, 32
Nijat Mehdiyev, Joerg Evermann, Peter Fettke (2017)
A multi-stage deep learning approach for business process event predictionProceedings of the 2017 IEEE 19th Conference on Business Informatics (CBI’17), 2017
A. Leontjeva, R. Conforti, Chiara Francescomarino, M. Dumas, F. Maggi (2015)
Complex Symbolic Sequence Encodings for Predictive Monitoring of Business Processes
Chih-Wei Hsu, Chih-Chung Chang, Chih-Jen Lin (2008)
A Practical Guide to Support Vector Classication
Alfonso Chamorro, Manuel Resinas, Antonio Ruiz-Cortés, M. Toro (2017)
Run-time prediction of business process indicators using evolutionary decision rulesExpert Syst. Appl., 87
George John, P. Langley (1995)
Estimating Continuous Distributions in Bayesian Classifiers
R. Conforti, M. Leoni, M. Rosa, Wil Aalst, A. Hofstede (2015)
A recommendation system for predicting risks across multiple business process instancesDecis. Support Syst., 69
R. Sun, C. Giles (2001)
Sequence learning: from recognition and prediction to sequential decision makingIEEE Intelligent Systems, 16
C. Cabanillas, Claudio Ciccio, J. Mendling, Anne Baumgraß (2014)
Predictive Task Monitoring for Business Processes
Richard O. Duda, Peter E. Hart, David G. Stork (2000)
Pattern Classification (2nd edWiley-Interscience
Y. Freund, R. Schapire (1997)
A decision-theoretic generalization of on-line learning and an application to boosting
Irene Teinemaa, Marlon Dumas, Marcello La Rosa, Fabrizio Maria Maggi (2019)
Outcome-oriented predictive process monitoring: Review and benchmarkACM Trans. Knowl. Discov. Data, 13
Stefan Schönig, C. Cabanillas, S. Jablonski, J. Mendling (2016)
A Framework for Efficiently Mining the Organisational Perspective of Business Processes (Extended Abstract)
A. Marqués, V. García, J. Sánchez (2012)
Exploring the behaviour of base classifiers in credit scoring ensemblesExpert Syst. Appl., 39
I. Verenich, M. Dumas, M. Rosa, F. Maggi, Chiara Francescomarino (2016)
Minimizing Overprocessing Waste in Business Processes via Predictive Activity Ordering
Xu-Cheng Yin, Kaizhu Huang, Hongwei Hao, Khalid Iqbal, Zhi-Bin Wang (2014)
A novel classifier ensemble method with sparsity and diversityNeurocomputing, 134
A. Márquez-Chamorro, Manuel Resinas, Antonio Ruiz-Cortés (2018)
Predictive Monitoring of Business Processes: A SurveyIEEE Transactions on Services Computing, 11
William Cohen (1995)
Fast Effective Rule Induction
Corinna Cortes, V. Vapnik (1995)
Support-Vector NetworksMachine Learning, 20
Chiara Francescomarino, M. Dumas, M. Federici, Chiara Ghidini, F. Maggi, Williams Rizzi, L. Simonetto (2018)
Genetic algorithms for hyperparameter optimization in predictive business process monitoringInf. Syst., 74
Stefan Schönig, Cristina Cabanillas, Stefan Jablonski, Jan Mendling (2016)
A framework for efficiently mining the organisational perspective of business processesDecis. Supp. Syst, 89
Liangzhao Zeng, Christoph Lingenfelder, H. Lei, Henry Chang (2008)
Event-Driven Quality of Service Prediction
D. Rom (1990)
A sequentially rejective test procedure based on a modified Bonferroni inequalityBiometrika, 77
D. Aha, D. Kibler, M. Albert (1991)
Instance-Based Learning AlgorithmsMachine Learning, 6
J. Abellán, C. Mantas (2014)
Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoringExpert Syst. Appl., 41
P. Leitner, B. Wetzstein, Florian Rosenberg, Anton Michlmayr, S. Dustdar, F. Leymann (2009)
Runtime Prediction of Service Level Agreement Violations for Composite Services
E. Frank, Stefan Kramer (2004)
Ensembles of nested dichotomies for multi-class problemsProceedings of the twenty-first international conference on Machine learning
(2019)
Enterprise, Business-Process and Information Systems Modeling, 352
Bokyoung Kang, Dongsoo Kim, Suk-Ho Kang (2012)
Periodic Performance Prediction for Real-time Business Process MonitoringInd. Manag. Data Syst., 112
Irene Teinemaa, M. Dumas, F. Maggi, Chiara Francescomarino (2016)
Predictive Business Process Monitoring with Structured and Unstructured Data
Tianqi Chen, Carlos Guestrin (2016)
XGBoost: A Scalable Tree Boosting SystemProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
L. Breiman (2001)
Random ForestsMachine Learning, 45
Christopher Klinkmüller, N. Beest, I. Weber (2018)
Towards Reliable Predictive Process Monitoring
L. Breiman (1996)
Bagging PredictorsMachine Learning, 24
Nijat Mehdiyev, Joerg Evermann, Peter Fettke (2017)
A Multi-stage Deep Learning Approach for Business Process Event Prediction2017 IEEE 19th Conference on Business Informatics (CBI), 01
Chiara Francescomarino, M. Dumas, F. Maggi, Irene Teinemaa (2015)
Clustering-Based Predictive Process MonitoringIEEE Transactions on Services Computing, 12
Merve Unuvar, Geetika Lakshmanan, Y. Doganata (2016)
Leveraging path information to generate predictions for parallel business processesKnowledge and Information Systems, 47
Vitali Melnikov, E. Hüllermeier (2018)
On the effectiveness of heuristics for learning nested dichotomies: an empirical analysisMachine Learning, 107
J. Abellán, Francisco Castellano (2017)
A comparative study on base classifiers in ensemble methods for credit scoringExpert Syst. Appl., 73
(2003)
A practical guide to support vector classification. Available at http://ntucsu.csie.ntu.edu.tw/∼cjlin/papers/guide/guide.pdf
G. Cooper, E. Herskovits (1992)
A Bayesian method for the induction of probabilistic networks from dataMachine Learning, 9
There is a growing need for empirical benchmarks that support researchers and practitioners in selecting the best machine learning technique for given prediction tasks. In this article, we consider the next event prediction task in business process predictive monitoring, and we extend our previously published benchmark by studying the impact on the performance of different encoding windows and of using ensemble schemes. The choice of whether to use ensembles and which scheme to use often depends on the type of data and classification task. While there is a general understanding that ensembles perform well in predictive monitoring of business processes, next event prediction is a task for which no other benchmarks involving ensembles are available. The proposed benchmark helps researchers to select a high-performing individual classifier or ensemble scheme given the variability at the case level of the event log under consideration. Experimental results show that choosing an optimal number of events for feature encoding is challenging, resulting in the need to consider each event log individually when selecting an optimal value. Ensemble schemes improve the performance of low-performing classifiers in this task, such as SVM, whereas high-performing classifiers, such as tree-based classifiers, are not better off when ensemble schemes are considered.
ACM Transactions on Intelligent Systems and Technology (TIST) – Association for Computing Machinery
Published: Sep 11, 2020
Keywords: Classifier ensembles
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