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Seppe K. L. M. vanden Broucke
Seppe vanden Broucke
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2020 – today
- 2024
- [j43]Bart Baesens
, Amy Adams, Rodrigo Pacheco-Ruiz
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
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
Validation set sampling strategies for predictive process monitoring. Inf. Syst. 121: 102330 (2024) - [j38]Björn Rafn Gunnarsson
LS-ICE: A Load State Intercase Encoding framework for improved predictive monitoring of business processes. Inf. Syst. 125: 102432 (2024) - [j37]Tim Verdonck
Special issue on feature engineering editorial. Mach. Learn. 113(7): 3917-3928 (2024) - [j36]Carlos Ortega Vázquez
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
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
Global conformance checking measures using shallow representation and deep learning. Eng. Appl. Artif. Intell. 123(Part): 106393 (2023) - [j33]Bing Zhu
A bagging-based selective ensemble model for churn prediction on imbalanced data. Expert Syst. Appl. 227: 120223 (2023) - [j32]Margot Geerts
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
Regularization oversampling for classification tasks: To exploit what you do not know. Inf. Sci. 635: 169-194 (2023) - [j30]Jari Peeperkorn
Can recurrent neural networks learn process model structure? J. Intell. Inf. Syst. 61(1): 27-51 (2023) - [j29]Björn Rafn Gunnarsson
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
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
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
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
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
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
Can recurrent neural networks learn process model structure? CoRR abs/2212.06430 (2022) - 2021
- [j26]Abdel-Jaouad Aberkane
Exploring Automated GDPR-Compliance in Requirements Engineering: A Systematic Mapping Study. IEEE Access 9: 66542-66559 (2021) - [j25]Björn Rafn Gunnarsson
Deep learning for credit scoring: Do or don't? Eur. J. Oper. Res. 295(1): 292-305 (2021) - [j24]Pieter De Koninck
Expert-driven trace clustering with instance-level constraints. Knowl. Inf. Syst. 63(5): 1197-1220 (2021) - [j23]Adriano Augusto
Optimization framework for DFG-based automated process discovery approaches. Softw. Syst. Model. 20(4): 1245-1270 (2021) - [c30]Jari Peeperkorn
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
An Experimental Investigation of Calibration Techniques for Imbalanced Data. IEEE Access 8: 127343-127352 (2020) - [j21]Sebastiaan Höppner, Eugen Stripling
Profit driven decision trees for churn prediction. Eur. J. Oper. Res. 284(3): 920-933 (2020) - [c29]Jari Peeperkorn
Conformance Checking Using Activity and Trace Embeddings. BPM (Forum) 2020: 105-121 - [c28]Jari Peeperkorn
Supervised Conformance Checking Using Recurrent Neural Network Classifiers. ICPM Workshops 2020: 175-187 - [c27]Steven Van Goidsenhoven
Predicting student success in a blended learning environment. LAK 2020: 17-25 - [c26]Carlos Ortega Vázquez
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
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
Predictive Process Monitoring in Operational Logistics: A Case Study in Aviation. Business Process Management Workshops 2019: 250-262 - [c24]Björn Rafn Gunnarsson
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
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
Profit maximizing logistic model for customer churn prediction using genetic algorithms. Swarm Evol. Comput. 40: 116-130 (2018) - [j12]Klaas Nelissen
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
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
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
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
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
Revising history for cost-informed process improvement. Computing 98(9): 895-921 (2016) - [j6]Xinwei Zhu, Seppe vanden Broucke, Guobin Zhu
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
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
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
Incorporating Negative Information in Process Discovery. BPM 2015: 126-143 - [c16]Eugen Stripling
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
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
Towards Location-Aware Process Modeling and Execution. Business Process Management Workshops 2014: 186-197 - [c14]Jochen De Weerdt
SECPI: Searching for Explanations for Clustered Process Instances. BPM 2014: 408-415 - [c13]Jochen De Weerdt
Bidimensional Process Discovery for Mining BPMN Models. Business Process Management Workshops 2014: 529-540 - [c12]Seppe K. L. M. vanden Broucke, Jan Vanthienen
Declarative process discovery with evolutionary computing. IEEE Congress on Evolutionary Computation 2014: 2412-2419 - [c11]Wei Zhe Low, Jochen De Weerdt
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
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
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
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
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
Improved Artificial Negative Event Generation to Enhance Process Event Logs. CAiSE 2012: 254-269 - [c1]Jochen De Weerdt
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
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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last updated on 2024-11-08 21:30 CET by the dblp team
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