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ORCIDSeppe K. L. M. vanden Broucke
Seppe vanden Broucke
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2020 – today
- 2024
[j43]Bart Baesens
, Amy Adams, Rodrigo Pacheco-Ruiz, Ann-Sophie Baesens, Seppe K. L. M. vanden Broucke:
Explainable Deep Learning to Classify Royal Navy Ships. IEEE Access 12: 1774-1785 (2024)- [j42]Margot Geerts, Seppe vanden Broucke, Jochen De Weerdt:
GeoRF: a geospatial random forest. Data Min. Knowl. Discov. 38(6): 3414-3448 (2024) - [j41]Manon Reusens, Alexander Stevens, Jonathan Tonglet, Johannes De Smedt, Wouter Verbeke, Seppe vanden Broucke, Bart Baesens:
Evaluating text classification: A benchmark study. Expert Syst. Appl. 254: 124302 (2024) - [j40]Yameng Guo, Seppe vanden Broucke:
Enhancing geospatial prediction models with feature engineering from road networks: a graph-driven approach. Int. J. Geogr. Inf. Sci. 38(8): 1611-1632 (2024) - [j39]Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt:
Validation set sampling strategies for predictive process monitoring. Inf. Syst. 121: 102330 (2024) - [j38]Björn Rafn Gunnarsson, Seppe vanden Broucke, Jochen De Weerdt:
LS-ICE: A Load State Intercase Encoding framework for improved predictive monitoring of business processes. Inf. Syst. 125: 102432 (2024) - [j37]Tim Verdonck, Bart Baesens, María Óskarsdóttir, Seppe vanden Broucke:
Special issue on feature engineering editorial. Mach. Learn. 113(7): 3917-3928 (2024) - [j36]Carlos Ortega Vázquez, Seppe vanden Broucke, Jochen De Weerdt:
Hellinger distance decision trees for PU learning in imbalanced data sets. Mach. Learn. 113(7): 4547-4578 (2024) - [c41]Brecht Wuyts, Seppe K. L. M. vanden Broucke, Jochen De Weerdt:
SuTraN: an Encoder-Decoder Transformer for Full-Context-Aware Suffix Prediction of Business Processes. ICPM 2024: 17-24 - 2023
- [j35]Carlos Ortega Vázquez, Seppe vanden Broucke, Jochen De Weerdt:
A two-step anomaly detection based method for PU classification in imbalanced data sets. Data Min. Knowl. Discov. 37(3): 1301-1325 (2023) - [j34]Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt:
Global conformance checking measures using shallow representation and deep learning. Eng. Appl. Artif. Intell. 123(Part): 106393 (2023) - [j33]Bing Zhu, Cheng Qian, Seppe vanden Broucke, Jin Xiao, Yuanyuan Li:
A bagging-based selective ensemble model for churn prediction on imbalanced data. Expert Syst. Appl. 227: 120223 (2023) - [j32]Margot Geerts, Seppe vanden Broucke, Jochen De Weerdt:
A Survey of Methods and Input Data Types for House Price Prediction. ISPRS Int. J. Geo Inf. 12(5): 200 (2023) - [j31]Lennert Van der Schraelen, Kristof Stouthuysen, Seppe K. L. M. vanden Broucke, Tim Verdonck:
Regularization oversampling for classification tasks: To exploit what you do not know. Inf. Sci. 635: 169-194 (2023) - [j30]Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt:
Can recurrent neural networks learn process model structure? J. Intell. Inf. Syst. 61(1): 27-51 (2023) - [j29]Björn Rafn Gunnarsson, Seppe vanden Broucke, Jochen De Weerdt:
A Direct Data Aware LSTM Neural Network Architecture for Complete Remaining Trace and Runtime Prediction. IEEE Trans. Serv. Comput. 16(4): 2330-2342 (2023) - [c40]Brecht Wuyts, Hans Weytjens, Seppe vanden Broucke, Jochen De Weerdt:
DyLoPro: Profiling the Dynamics of Event Logs. BPM 2023: 146-162 - [c39]Jan Niklas Adams, Jari Peeperkorn, Tobias Brockhoff, Isabelle Terrier, Heiko Göhner, Merih Seran Uysal, Seppe vanden Broucke, Jochen De Weerdt, Wil M. P. van der Aalst:
Discovering high-quality process models despite data scarcity. ER (Companion) 2023 - [c38]Margot Geerts, Seppe vanden Broucke, Jochen De Weerdt:
An Evolutionary Geospatial Regression Tree. STRL@IJCAI 2023 - [c37]Yameng Guo, Seppe vanden Broucke:
Geospatial Prediction Using Road Topology: A Graph-based Perspective. STRL@IJCAI 2023 - [c36]Abdel-Jaouad Aberkane, Seppe vanden Broucke, Geert Poels:
Toward Data Protection by Design: Assessing the Current State of GDPR Disclosure in Web Applications. REW 2023: 218-223 - [i5]Jan Niklas Adams, Jari Peeperkorn, Tobias Brockhoff, Isabelle Terrier, Heiko Göhner, Merih Seran Uysal, Seppe vanden Broucke, Jochen De Weerdt, Wil M. P. van der Aalst:
Discovering High-Quality Process Models Despite Data Scarcity. CoRR abs/2310.11332 (2023) - 2022
- [j28]Bing Zhu, Xin Pan, Seppe vanden Broucke, Jin Xiao:
A GAN-based hybrid sampling method for imbalanced customer classification. Inf. Sci. 609: 1397-1411 (2022) - [j27]Faruk Hasic, Johannes De Smedt, Seppe vanden Broucke, Estefanía Serral:
Decision as a Service (DaaS): A Service-Oriented Architecture Approach for Decisions in Processes. IEEE Trans. Serv. Comput. 15(2): 904-917 (2022) - [c35]Jari Peeperkorn, Carlos Ortega Vázquez, Alexander Stevens, Johannes De Smedt, Seppe vanden Broucke, Jochen De Weerdt:
Outcome-Oriented Predictive Process Monitoring on Positive and Unlabelled Event Logs. ICPM Workshops 2022: 255-268 - [c34]Björn Rafn Gunnarsson, Jochen De Weerdt, Seppe vanden Broucke:
A framework for encoding the multi-location load state of a business process. PMAI@IJCAI 2022: 13-24 - [c33]Carlos Ortega Vázquez, Jochen De Weerdt, Seppe vanden Broucke:
The Hidden Cost of Fraud: An Instance-Dependent Cost-Sensitive Approach for Positive and Unlabeled Learning. LIDTA 2022: 53-67 - [c32]Margot Geerts, Kiran Shaikh, Jochen De Weerdt, Seppe vanden Broucke:
Predicting the State of a House Using Google Street View - An Analysis of Deep Binary Classification Models for the Assessment of the Quality of Flemish Houses. RCIS 2022: 703-710 - [c31]Abdel-Jaouad Aberkane, Seppe vanden Broucke, Geert Poels:
Investigating Organizational Factors Associated with GDPR Noncompliance using Privacy Policies: A Machine Learning Approach. TPS-ISA 2022: 107-113 - [i4]Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt:
Can deep neural networks learn process model structure? An assessment framework and analysis. CoRR abs/2202.11985 (2022) - [i3]Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt:
Can recurrent neural networks learn process model structure? CoRR abs/2212.06430 (2022) - 2021
- [j26]Abdel-Jaouad Aberkane, Geert Poels, Seppe vanden Broucke:
Exploring Automated GDPR-Compliance in Requirements Engineering: A Systematic Mapping Study. IEEE Access 9: 66542-66559 (2021) - [j25]Björn Rafn Gunnarsson, Seppe vanden Broucke, Bart Baesens, María Óskarsdóttir, Wilfried Lemahieu:
Deep learning for credit scoring: Do or don't? Eur. J. Oper. Res. 295(1): 292-305 (2021) - [j24]Pieter De Koninck, Klaas Nelissen, Seppe vanden Broucke, Bart Baesens, Monique Snoeck, Jochen De Weerdt:
Expert-driven trace clustering with instance-level constraints. Knowl. Inf. Syst. 63(5): 1197-1220 (2021) - [j23]Adriano Augusto, Marlon Dumas, Marcello La Rosa, Sander J. J. Leemans, Seppe K. L. M. vanden Broucke:
Optimization framework for DFG-based automated process discovery approaches. Softw. Syst. Model. 20(4): 1245-1270 (2021) - [c30]Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt:
Can Deep Neural Networks Learn Process Model Structure? An Assessment Framework and Analysis. ICPM Workshops 2021: 127-139 - [i2]Pieter De Koninck, Klaas Nelissen, Seppe vanden Broucke, Bart Baesens, Monique Snoeck, Jochen De Weerdt:
Expert-driven Trace Clustering with Instance-level Constraints. CoRR abs/2110.06703 (2021) - 2020
- [j22]Lanlan Huang, Junkai Zhao, Bing Zhu, Hao Chen, Seppe K. L. M. vanden Broucke:
An Experimental Investigation of Calibration Techniques for Imbalanced Data. IEEE Access 8: 127343-127352 (2020) - [j21]Sebastiaan Höppner, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Tim Verdonck:
Profit driven decision trees for churn prediction. Eur. J. Oper. Res. 284(3): 920-933 (2020) - [c29]Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt:
Conformance Checking Using Activity and Trace Embeddings. BPM (Forum) 2020: 105-121 - [c28]Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt:
Supervised Conformance Checking Using Recurrent Neural Network Classifiers. ICPM Workshops 2020: 175-187 - [c27]Steven Van Goidsenhoven, Daria Bogdanova, Galina Deeva, Seppe vanden Broucke, Jochen De Weerdt, Monique Snoeck:
Predicting student success in a blended learning environment. LAK 2020: 17-25 - [c26]Carlos Ortega Vázquez, Sandra Mitrovic, Jochen De Weerdt, Seppe vanden Broucke:
A Comparative Study of Representation Learning Techniques for Dynamic Networks. WorldCIST (3) 2020: 523-530
2010 – 2019
- 2019
- [j20]Jasmien Lismont, Tine Van Calster, María Óskarsdóttir, Seppe vanden Broucke, Bart Baesens, Wilfried Lemahieu, Jan Vanthienen:
Closing the Gap Between Experts and Novices Using Analytics-as-a-Service: An Experimental Study. Bus. Inf. Syst. Eng. 61(6): 679-693 (2019) - [j19]Johannes De Smedt, Faruk Hasic, Seppe K. L. M. vanden Broucke, Jan Vanthienen:
Holistic discovery of decision models from process execution data. Knowl. Based Syst. 183 (2019) - [j18]Bing Zhu, Zihan Gao, Junkai Zhao, Seppe K. L. M. vanden Broucke:
IRIC: An R library for binary imbalanced classification. SoftwareX 10: 100341 (2019) - [c25]Björn Rafn Gunnarsson, Seppe K. L. M. vanden Broucke, Jochen De Weerdt:
Predictive Process Monitoring in Operational Logistics: A Case Study in Aviation. Business Process Management Workshops 2019: 250-262 - [c24]Björn Rafn Gunnarsson, Seppe vanden Broucke, Jochen De Weerdt:
Optimizing Marketing Campaign Targeting Using Uncertainty-Based Predictive Modelling. ICDM Workshops 2019: 326-332 - 2018
- [j17]Wilfried Lemahieu, Seppe vanden Broucke, Bart Baesens:
An Interview with Bart Baesens, One of the Authors of Principles of Database Management. Big Data 6(2): 69-71 (2018) - [j16]Eugen Stripling, Bart Baesens, Barak Chizi, Seppe vanden Broucke:
Isolation-based conditional anomaly detection on mixed-attribute data to uncover workers' compensation fraud. Decis. Support Syst. 111: 13-26 (2018) - [j15]Hernán Ponce de León, Lucio Nardelli, Josep Carmona, Seppe K. L. M. vanden Broucke:
Incorporating negative information to process discovery of complex systems. Inf. Sci. 422: 480-496 (2018) - [j14]Bing Zhu, Bart Baesens, Aimée Backiel, Seppe K. L. M. vanden Broucke:
Benchmarking sampling techniques for imbalance learning in churn prediction. J. Oper. Res. Soc. 69(1): 49-65 (2018) - [j13]Eugen Stripling, Seppe vanden Broucke, Katrien Antonio, Bart Baesens, Monique Snoeck:
Profit maximizing logistic model for customer churn prediction using genetic algorithms. Swarm Evol. Comput. 40: 116-130 (2018) - [j12]Klaas Nelissen, Monique Snoeck, Seppe K. L. M. vanden Broucke, Bart Baesens:
Swipe and Tell: Using Implicit Feedback to Predict User Engagement on Tablets. ACM Trans. Inf. Syst. 36(4): 35:1-35:36 (2018) - [c23]Pieter De Koninck, Seppe vanden Broucke, Jochen De Weerdt:
act2vec, trace2vec, log2vec, and model2vec: Representation Learning for Business Processes. BPM 2018: 305-321 - [c22]Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav S. Sukhatme:
Profit Maximizing Logistic Regression Modeling for Credit Scoring. DSW 2018: 125-129 - 2017
- [j11]Pieter De Koninck, Jochen De Weerdt, Seppe K. L. M. vanden Broucke:
Explaining clusterings of process instances. Data Min. Knowl. Discov. 31(3): 774-808 (2017) - [j10]Seppe K. L. M. vanden Broucke, Jochen De Weerdt:
Fodina: A robust and flexible heuristic process discovery technique. Decis. Support Syst. 100: 109-118 (2017) - [j9]Bing Zhu, Bart Baesens, Seppe K. L. M. vanden Broucke:
An empirical comparison of techniques for the class imbalance problem in churn prediction. Inf. Sci. 408: 84-99 (2017) - [c21]Johannes De Smedt, Faruk Hasic, Seppe K. L. M. vanden Broucke, Jan Vanthienen:
Towards a Holistic Discovery of Decisions in Process-Aware Information Systems. BPM 2017: 183-199 - [c20]Pieter De Koninck, Klaas Nelissen, Bart Baesens, Seppe vanden Broucke, Monique Snoeck, Jochen De Weerdt:
An Approach for Incorporating Expert Knowledge in Trace Clustering. CAiSE 2017: 561-576 - [c19]Bing Zhu, Seppe vanden Broucke, Bart Baesens, Sebastián Maldonado:
Improving Resampling-based Ensemble in Churn Prediction. LIDTA@PKDD/ECML 2017: 79-91 - [i1]Sebastiaan Höppner, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Tim Verdonck:
Profit Driven Decision Trees for Churn Prediction. CoRR abs/1712.08101 (2017) - 2016
- [j8]Jasmien Lismont, Anne-Sophie Janssens, Irina Odnoletkova, Seppe vanden Broucke, Filip Caron, Jan Vanthienen:
A guide for the application of analytics on healthcare processes: A dynamic view on patient pathways. Comput. Biol. Medicine 77: 125-134 (2016) - [j7]Wei Zhe Low, Seppe K. L. M. vanden Broucke, Moe Thandar Wynn, Arthur H. M. ter Hofstede, Jochen De Weerdt, Wil M. P. van der Aalst:
Revising history for cost-informed process improvement. Computing 98(9): 895-921 (2016) - [j6]Xinwei Zhu, Seppe vanden Broucke, Guobin Zhu, Jan Vanthienen, Bart Baesens:
Enabling flexible location-aware business process modeling and execution. Decis. Support Syst. 83: 1-9 (2016) - [j5]Seppe K. L. M. vanden Broucke, Filip Caron, Jasmien Lismont, Jan Vanthienen, Bart Baesens:
On the gap between reality and registration: a business event analysis classification framework. Inf. Technol. Manag. 17(4): 393-410 (2016) - [c18]Johannes De Smedt, Seppe K. L. M. vanden Broucke, Josué Obregon, Aekyung Kim, Jae-Yoon Jung, Jan Vanthienen:
Decision Mining in a Broader Context: An Overview of the Current Landscape and Future Directions. Business Process Management Workshops 2016: 197-207 - 2015
- [c17]Hernán Ponce de León, Josep Carmona, Seppe K. L. M. vanden Broucke:
Incorporating Negative Information in Process Discovery. BPM 2015: 126-143 - [c16]Eugen Stripling, Seppe vanden Broucke, Katrien Antonio, Bart Baesens, Monique Snoeck:
Profit maximizing logistic regression modeling for customer churn prediction. DSAA 2015: 1-10 - 2014
- [b1]Seppe vanden Broucke:
Advances in Process Mining: Artificial negative events and othertechniques. Katholieke Universiteit Leuven, Belgium, 2014 - [j4]Alex Seret, Seppe K. L. M. vanden Broucke, Bart Baesens, Jan Vanthienen:
A dynamic understanding of customer behavior processes based on clustering and sequence mining. Expert Syst. Appl. 41(10): 4648-4657 (2014) - [j3]Seppe K. L. M. vanden Broucke, Jochen De Weerdt, Jan Vanthienen, Bart Baesens:
Determining Process Model Precision and Generalization with Weighted Artificial Negative Events. IEEE Trans. Knowl. Data Eng. 26(8): 1877-1889 (2014) - [c15]Xinwei Zhu, Guobin Zhu, Seppe K. L. M. vanden Broucke, Jan Vanthienen, Bart Baesens:
Towards Location-Aware Process Modeling and Execution. Business Process Management Workshops 2014: 186-197 - [c14]Jochen De Weerdt, Seppe K. L. M. vanden Broucke:
SECPI: Searching for Explanations for Clustered Process Instances. BPM 2014: 408-415 - [c13]Jochen De Weerdt, Seppe K. L. M. vanden Broucke, Filip Caron:
Bidimensional Process Discovery for Mining BPMN Models. Business Process Management Workshops 2014: 529-540 - [c12]Seppe K. L. M. vanden Broucke, Jan Vanthienen, Bart Baesens:
Declarative process discovery with evolutionary computing. IEEE Congress on Evolutionary Computation 2014: 2412-2419 - [c11]Wei Zhe Low, Jochen De Weerdt, Moe Thandar Wynn, Arthur H. M. ter Hofstede, Wil M. P. van der Aalst, Seppe K. L. M. vanden Broucke:
Perturbing event logs to identify cost reduction opportunities: A genetic algorithm-based approach. IEEE Congress on Evolutionary Computation 2014: 2428-2435 - [c10]Xinwei Zhu, Guobin Zhu, Seppe vanden Broucke, Jan Recker:
On Merging Business Process Management and Geographic Information Systems: Modeling and Execution of Ecological Concerns in Processes. GRMSE 2014: 486-496 - [c9]Seppe K. L. M. vanden Broucke, Jorge Munoz-Gama, Josep Carmona, Bart Baesens, Jan Vanthienen:
Event-Based Real-Time Decomposed Conformance Analysis. OTM Conferences 2014: 345-363 - 2013
- [j2]Jochen De Weerdt, Seppe K. L. M. vanden Broucke, Jan Vanthienen, Bart Baesens:
Active Trace Clustering for Improved Process Discovery. IEEE Trans. Knowl. Data Eng. 25(12): 2708-2720 (2013) - [c8]Seppe K. L. M. vanden Broucke, Cédric Delvaux, João Freitas, Taisiia Rogova, Jan Vanthienen, Bart Baesens:
Uncovering the Relationship Between Event Log Characteristics and Process Discovery Techniques. Business Process Management Workshops 2013: 41-53 - [c7]Seppe K. L. M. vanden Broucke, Filip Caron, Jan Vanthienen, Bart Baesens:
Validating and Enhancing Declarative Business Process Models Based on Allowed and Non-occurring Past Behavior. Business Process Management Workshops 2013: 212-223 - [c6]Seppe vanden Broucke, Jan Vanthienen, Bart Baesens:
Volvo IT Belgium VINST. BPIC@BPM 2013 - [c5]Alex Seret, Seppe K. L. M. vanden Broucke, Bart Baesens, Jan Vanthienen:
An Exploratory Approach for Understanding Customer Behavior Processes Based on Clustering and Sequence Mining. Business Process Management Workshops 2013: 237-248 - [c4]Seppe K. L. M. vanden Broucke, Jochen De Weerdt, Jan Vanthienen, Bart Baesens:
A comprehensive benchmarking framework (CoBeFra) for conformance analysis between procedural process models and event logs in ProM. CIDM 2013: 254-261 - 2012
- [c3]Filip Caron, Seppe K. L. M. vanden Broucke, Jan Vanthienen, Bart Baesens:
On the Distinction between Truthful, Invisible, False and Unobserved Events An Event Existence Classification Framework and the Impact on Business Process Analytics Related Research Areas. AMCIS 2012 - [c2]Seppe K. L. M. vanden Broucke, Jochen De Weerdt, Bart Baesens, Jan Vanthienen:
Improved Artificial Negative Event Generation to Enhance Process Event Logs. CAiSE 2012: 254-269 - [c1]Jochen De Weerdt, Seppe K. L. M. vanden Broucke, Jan Vanthienen, Bart Baesens:
Leveraging process discovery with trace clustering and text mining for intelligent analysis of incident management processes. IEEE Congress on Evolutionary Computation 2012: 1-8 - 2011
- [j1]Tim Van den Bulcke, Paul Vanden Broucke, Viviane Van Hoof, Kristien Wouters, Seppe vanden Broucke, Geert Smits, Elke Smits, Sam Proesmans, Toon Van Genechten, François Eyskens:
Data mining methods for classification of Medium-Chain Acyl-CoA dehydrogenase deficiency (MCADD) using non-derivatized tandem MS neonatal screening data. J. Biomed. Informatics 44(2): 319-325 (2011)
Coauthor Index
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