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Dirk Van den Poel
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2020 – today
- 2024
- [j88]Bram Janssens
, Matthias Bogaert
B2Boost: instance-dependent profit-driven modelling of B2B churn. Ann. Oper. Res. 341(1): 267-293 (2024) - [j87]Bram Janssens
360 Degrees rumor detection: When explanations got some explaining to do. Eur. J. Oper. Res. 317(2): 366-381 (2024) - 2022
- [e2]Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan:
IEEE International Conference on Big Data, Big Data 2022, Osaka, Japan, December 17-20, 2022. IEEE 2022, ISBN 978-1-6654-8045-1 [contents] - 2021
- [j86]Giselle van Dongen
A Performance Analysis of Fault Recovery in Stream Processing Frameworks. IEEE Access 9: 93745-93763 (2021) - [j85]Giselle van Dongen
Influencing Factors in the Scalability of Distributed Stream Processing Jobs. IEEE Access 9: 109413-109431 (2021) - [j84]Bram Steurtewagen
Adding interpretability to predictive maintenance by machine learning on sensor data. Comput. Chem. Eng. 152: 107381 (2021) - [j83]Arno Liseune
Predicting the milk yield curve of dairy cows in the subsequent lactation period using deep learning. Comput. Electron. Agric. 180: 105904 (2021) - [j82]Arno Liseune, Dirk Van den Poel, Peter R. Hut, Frank J. C. M. van Eerdenburg, Miel Hostens
Leveraging sequential information from multivariate behavioral sensor data to predict the moment of calving in dairy cattle using deep learning. Comput. Electron. Agric. 191: 106566 (2021) - [j81]Matthias Bogaert, Michel Ballings
Predicting Self-declared Movie Watching Behavior Using Facebook Data and Information-Fusion Sensitivity Analysis. Decis. Sci. 52(3): 776-810 (2021) - [j80]Lisa Schetgen
Predicting donation behavior: Acquisition modeling in the nonprofit sector using Facebook data. Decis. Support Syst. 141: 113446 (2021) - [j79]Matthias Bogaert, Michel Ballings, Dirk Van den Poel
Box office sales and social media: A cross-platform comparison of predictive ability and mechanisms. Decis. Support Syst. 147: 113517 (2021) - [j78]Anneleen Rummens
Do Mobile Phone Data Provide a Better Denominator in Crime Rates and Improve Spatiotemporal Predictions of Crime? ISPRS Int. J. Geo Inf. 10(6): 369 (2021) - 2020
- [j77]Bram Steurtewagen
Machine learning refinery sensor data to predict catalyst saturation levels. Comput. Chem. Eng. 134: 106722 (2020) - [j76]Arno Liseune
Leveraging latent representations for milk yield prediction and interpolation using deep learning. Comput. Electron. Agric. 175: 105600 (2020) - [j75]Giselle van Dongen
Evaluation of Stream Processing Frameworks. IEEE Trans. Parallel Distributed Syst. 31(8): 1845-1858 (2020)
2010 – 2019
- 2019
- [j74]Matthias Bogaert
Evaluating multi-label classifiers and recommender systems in the financial service sector. Eur. J. Oper. Res. 279(2): 620-634 (2019) - 2018
- [j73]Matthias Bogaert, Michel Ballings, Dirk Van den Poel
Evaluating the importance of different communication types in romantic tie prediction on social media. Ann. Oper. Res. 263(1-2): 501-527 (2018) - [c23]Giselle van Dongen
Latency Measurement of Fine-Grained Operations in Benchmarking Distributed Stream Processing Frameworks. BigData Congress 2018: 247-250 - 2017
- [j72]Matthias Bogaert
Identifying Soccer Players on Facebook Through Predictive Analytics. Decis. Anal. 14(4): 274-297 (2017) - [j71]Andrey Volkov, Dries F. Benoit
Incorporating sequential information in bankruptcy prediction with predictors based on Markov for discrimination. Decis. Support Syst. 98: 59-68 (2017) - [j70]Matthijs Meire
The added value of social media data in B2B customer acquisition systems: A real-life experiment. Decis. Support Syst. 104: 26-37 (2017) - 2016
- [j69]Matthias Bogaert, Michel Ballings, Dirk Van den Poel
The added value of Facebook friends data in event attendance prediction. Decis. Support Syst. 82: 26-34 (2016) - [j68]Jeroen D'Haen, Dirk Van den Poel
Integrating expert knowledge and multilingual web crawling data in a lead qualification system. Decis. Support Syst. 82: 69-78 (2016) - [j67]Matthijs Meire
The added value of auxiliary data in sentiment analysis of Facebook posts. Decis. Support Syst. 89: 98-112 (2016) - [j66]Dirk Thorleuchter, Dirk Van den Poel
Identification of interdisciplinary ideas. Inf. Process. Manag. 52(6): 1074-1085 (2016) - [c22]Dirk Van den Poel
Equity price direction prediction for day trading: Ensemble classification using technical analysis indicators with interaction effects. CEC 2016: 3455-3462 - 2015
- [j65]Michel Ballings, Dirk Van den Poel
CRM in social media: Predicting increases in Facebook usage frequency. Eur. J. Oper. Res. 244(1): 248-260 (2015) - [j64]Michel Ballings, Dirk Van den Poel
Evaluating multiple classifiers for stock price direction prediction. Expert Syst. Appl. 42(20): 7046-7056 (2015) - 2014
- [j63]Kamini Venkatesh, Vadlamani Ravi, Anita Prinzie, Dirk Van den Poel
Cash demand forecasting in ATMs by clustering and neural networks. Eur. J. Oper. Res. 232(2): 383-392 (2014) - [j62]Dirk Thorleuchter
Quantitative cross impact analysis with latent semantic indexing. Expert Syst. Appl. 41(2): 406-411 (2014) - [j61]Dirk Thorleuchter
Semantic compared cross impact analysis. Expert Syst. Appl. 41(7): 3477-3483 (2014) - [j60]Dirk Thorleuchter, Tobias Scheja, Dirk Van den Poel
Semantic weak signal tracing. Expert Syst. Appl. 41(11): 5009-5016 (2014) - [c21]Dirk Thorleuchter, Dirk Van den Poel
Using Text Summarizing to Support Planning of Research and Development. WorldCIST (1) 2014: 23-29 - 2013
- [j59]Dirk Thorleuchter
Technology classification with latent semantic indexing. Expert Syst. Appl. 40(5): 1786-1795 (2013) - [j58]Jeroen D'Haen, Dirk Van den Poel
Predicting customer profitability during acquisition: Finding the optimal combination of data source and data mining technique. Expert Syst. Appl. 40(6): 2007-2012 (2013) - [j57]Michel Ballings
Kernel Factory: An ensemble of kernel machines. Expert Syst. Appl. 40(8): 2904-2913 (2013) - [j56]Dirk Thorleuchter
Protecting research and technology from espionage. Expert Syst. Appl. 40(9): 3432-3440 (2013) - [j55]Dirk Thorleuchter
Web mining based extraction of problem solution ideas. Expert Syst. Appl. 40(10): 3961-3969 (2013) - [j54]Dirk Thorleuchter
Weak signal identification with semantic web mining. Expert Syst. Appl. 40(12): 4978-4985 (2013) - [j53]Philippe Baecke, Dirk Van den Poel
Improving customer acquisition models by incorporating spatial autocorrelation at different levels of granularity. J. Intell. Inf. Syst. 41(1): 73-90 (2013) - [j52]Vera L. Miguéis
Enhanced decision support in credit scoring using Bayesian binary quantile regression. J. Oper. Res. Soc. 64(9): 1374-1383 (2013) - [c20]Michel Ballings, Dirk Van den Poel
Using Eye-Tracking Data of Advertisement Viewing Behavior to Predict Customer Churn. ICDM Workshops 2013: 201-205 - [c19]Dirk Thorleuchter
Analyzing Website Content for Improved R&T Collaboration Planning. WorldCIST 2013: 567-573 - [e1]Berthold Lausen, Dirk Van den Poel, Alfred Ultsch:
Algorithms from and for Nature and Life - Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization, Springer 2013, ISBN 978-3-319-00034-3 [contents] - 2012
- [j51]Vera L. Miguéis
Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences. Adv. Data Anal. Classif. 6(4): 337-353 (2012) - [j50]Dirk Van den Poel:
Book Review: Ensemble Methods: Foundations and Algorithms. IEEE Intell. Informatics Bull. 13(1): 33-34 (2012) - [j49]Dirk Thorleuchter
Analyzing existing customers' websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing. Expert Syst. Appl. 39(3): 2597-2605 (2012) - [j48]Koen W. De Bock
Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models. Expert Syst. Appl. 39(8): 6816-6826 (2012) - [j47]Vera L. Miguéis
Modeling partial customer churn: On the value of first product-category purchase sequences. Expert Syst. Appl. 39(12): 11250-11256 (2012) - [j46]Dries F. Benoit
Improving customer retention in financial services using kinship network information. Expert Syst. Appl. 39(13): 11435-11442 (2012) - [j45]Philippe Baecke, Dirk Van den Poel
Including spatial interdependence in customer acquisition models: A cross-category comparison. Expert Syst. Appl. 39(15): 12105-12113 (2012) - [j44]Dirk Thorleuchter
Predicting e-commerce company success by mining the text of its publicly-accessible website. Expert Syst. Appl. 39(17): 13026-13034 (2012) - [j43]Griet Alice Verhaert, Dirk Van den Poel
The role of seed money and threshold size in optimizing fundraising campaigns: Past behavior matters! Expert Syst. Appl. 39(18): 13075-13084 (2012) - [j42]Dirk Thorleuchter
Improved multilevel security with latent semantic indexing. Expert Syst. Appl. 39(18): 13462-13471 (2012) - [j41]Michel Ballings
Customer event history for churn prediction: How long is long enough? Expert Syst. Appl. 39(18): 13517-13522 (2012) - [c18]Dirk Thorleuchter
Improved Emergency Management by a Loosely Coupled Logistic System. Future Security 2012: 5-8 - [c17]Dirk Thorleuchter
Using NMF for Analyzing War Logs. Future Security 2012: 73-76 - [c16]Michel Ballings, Dirk Van den Poel
The Dangers of Using Intention as a Surrogate for Retention in Brand Positioning Decision Support Systems. GfKl 2012: 181-188 - [c15]Jeroen D'Haen, Dirk Van den Poel
Temporary Staffing Services: A Data Mining Perspective. ICDM Workshops 2012: 287-292 - [c14]Andrey Volkov, Dirk Van den Poel
Extracting Information from Sequences of Financial Ratios with Markov for Discrimination: An Application to Bankruptcy Prediction. ICDM Workshops 2012: 340-343 - [c13]Michel Ballings, Dirk Van den Poel
Improving Customer Churn Prediction by Data Augmentation Using Pictorial Stimulus-Choice Data. IS-MiS 2012: 217-226 - [c12]Dirk Thorleuchter
Using Webcrawling of Publicly Available Websites to Assess E-commerce Relationships. SRII Global Conference 2012: 402-410 - 2011
- [j40]Koen W. De Bock
An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction. Expert Syst. Appl. 38(10): 12293-12301 (2011) - [j39]Anita Prinzie, Dirk Van den Poel
Modeling complex longitudinal consumer behavior with Dynamic Bayesian networks: an Acquisition Pattern Analysis application. J. Intell. Inf. Syst. 36(3): 283-304 (2011) - [j38]Philippe Baecke, Dirk Van den Poel
Data augmentation by predicting spending pleasure using commercially available external data. J. Intell. Inf. Syst. 36(3): 367-383 (2011) - [c11]Michel Ballings
RFM Variables Revisited Using Quantile Regression. ICDM Workshops 2011: 1163-1169 - [c10]Philippe Baecke, Dirk Van den Poel
Incorporating Neighborhood Effects in Customer Relationship Management Models. ISMIS 2011: 90-95 - 2010
- [j37]Koen W. De Bock
Ensemble classification based on generalized additive models. Comput. Stat. Data Anal. 54(6): 1535-1546 (2010) - [j36]Kristof Coussement
Improved marketing decision making in a customer churn prediction context using generalized additive models. Expert Syst. Appl. 37(3): 2132-2143 (2010) - [j35]Dirk Thorleuchter
Mining ideas from textual information. Expert Syst. Appl. 37(10): 7182-7188 (2010) - [j34]Koen W. De Bock
Predicting Website Audience Demographics forWeb Advertising Targeting Using Multi-Website Clickstream Data. Fundam. Informaticae 98(1): 49-70 (2010) - [j33]Philippe Baecke, Dirk Van den Poel
Improving Purchasing Behavior Predictions by Data Augmentation with Situational Variables. Int. J. Inf. Technol. Decis. Mak. 9(6): 853-872 (2010) - [c9]Koen W. De Bock
Ensembles of Probability Estimation Trees for Customer Churn Prediction. IEA/AIE (2) 2010: 57-66 - [c8]Dirk Thorleuchter
Extracting Consumers Needs for New Products - A Web Mining Approach. WKDD 2010: 440-443
2000 – 2009
- 2009
- [j32]Jonathan Burez, Dirk Van den Poel
Handling class imbalance in customer churn prediction. Expert Syst. Appl. 36(3): 4626-4636 (2009) - [j31]Kristof Coussement
Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers. Expert Syst. Appl. 36(3): 6127-6134 (2009) - [j30]Dries F. Benoit
Benefits of quantile regression for the analysis of customer lifetime value in a contractual setting: An application in financial services. Expert Syst. Appl. 36(7): 10475-10484 (2009) - 2008
- [j29]Kristof Coussement
Improving customer complaint management by automatic email classification using linguistic style features as predictors. Decis. Support Syst. 44(4): 870-882 (2008) - [j28]Kristof Coussement
Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques. Expert Syst. Appl. 34(1): 313-327 (2008) - [j27]Anita Prinzie, Dirk Van den Poel
Random Forests for multiclass classification: Random MultiNomial Logit. Expert Syst. Appl. 34(3): 1721-1732 (2008) - [j26]Jonathan Burez, Dirk Van den Poel
Separating financial from commercial customer churn: A modeling step towards resolving the conflict between the sales and credit department. Expert Syst. Appl. 35(1-2): 497-514 (2008) - [j25]Kristof Coussement
Integrating the voice of customers through call center emails into a decision support system for churn prediction. Inf. Manag. 45(3): 164-174 (2008) - [c7]Anita Prinzie, Dirk Van den Poel
Dynamic Bayesian Networks for Acquisition Pattern Analysis: A Financial-Services Cross-Sell Application. PAKDD Workshops 2008: 123-133 - 2007
- [j24]Anita Prinzie, Dirk Van den Poel
Predicting home-appliance acquisition sequences: Markov/Markov for Discrimination and survival analysis for modeling sequential information in NPTB models. Decis. Support Syst. 44(1): 28-45 (2007) - [j23]Bart Larivière
Banking behaviour after the lifecycle event of "moving in together": An exploratory study of the role of marketing investments. Eur. J. Oper. Res. 183(1): 345-369 (2007) - [j22]Wouter Buckinx, Geert Verstraeten, Dirk Van den Poel
Predicting customer loyalty using the internal transactional database. Expert Syst. Appl. 32(1): 125-134 (2007) - [j21]Jonathan Burez, Dirk Van den Poel
CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services. Expert Syst. Appl. 32(2): 277-288 (2007) - [c6]Anita Prinzie, Dirk Van den Poel
Random Multiclass Classification: Generalizing Random Forests to Random MNL and Random NB. DEXA 2007: 349-358 - 2006
- [j20]Anita Prinzie, Dirk Van den Poel
Incorporating sequential information into traditional classification models by using an element/position-sensitive SAM. Decis. Support Syst. 42(2): 508-526 (2006) - [j19]Anita Prinzie, Dirk Van den Poel
Investigating purchasing-sequence patterns for financial services using Markov, MTD and MTDg models. Eur. J. Oper. Res. 170(3): 710-734 (2006) - [j18]Tony Van Gestel, Bart Baesens, Johan A. K. Suykens
Bayesian kernel based classification for financial distress detection. Eur. J. Oper. Res. 172(3): 979-1003 (2006) - [c5]Geert Verstraeten, Dirk Van den Poel:
Using Predicted Outcome Stratified Sampling to Reduce the Variability in Predictive Performance of a One-Shot Train-and-Test Split for Individual Customer Predictions. ICDM (Posters) 2006: 214-224 - [c4]Anita Prinzie, Dirk Van den Poel
Exploiting Randomness for Feature Selection in Multinomial Logit: A CRM Cross-Sell Application. ICDM 2006: 310-323 - 2005
- [j17]Wouter Buckinx, Dirk Van den Poel
Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting. Eur. J. Oper. Res. 164(1): 252-268 (2005) - [j16]Dirk Van den Poel
Predicting online-purchasing behaviour. Eur. J. Oper. Res. 166(2): 557-575 (2005) - [j15]Bernd Vindevogel, Dirk Van den Poel
Why promotion strategies based on market basket analysis do not work. Expert Syst. Appl. 28(3): 583-590 (2005) - [j14]Bart Larivière
Predicting customer retention and profitability by using random forests and regression forests techniques. Expert Syst. Appl. 29(2): 472-484 (2005) - [j13]Anita Prinzie, Dirk Van den Poel
Constrained optimization of data-mining problems to improve model performance: A direct-marketing application. Expert Syst. Appl. 29(3): 630-640 (2005) - [j12]Bart Larivière
Investigating the post-complaint period by means of survival analysis. Expert Syst. Appl. 29(3): 667-677 (2005) - [j11]Geert Verstraeten, Dirk Van den Poel
The impact of sample bias on consumer credit scoring performance and profitability. J. Oper. Res. Soc. 56(8): 981-992 (2005) - [j10]Bart Baesens, Tony Van Gestel, M. Stepanova, Dirk Van den Poel
Neural network survival analysis for personal loan data. J. Oper. Res. Soc. 56(9): 1089-1098 (2005) - 2004
- [j9]Bart Baesens, Geert Verstraeten, Dirk Van den Poel
Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers. Eur. J. Oper. Res. 156(2): 508-523 (2004) - [j8]Dirk Van den Poel
Customer attrition analysis for financial services using proportional hazard models. Eur. J. Oper. Res. 157(1): 196-217 (2004) - [j7]Wouter Buckinx, Elke Moons, Dirk Van den Poel
Customer-adapted coupon targeting using feature selection. Expert Syst. Appl. 26(4): 509-518 (2004) - [j6]Dirk Van den Poel
Direct and indirect effects of retail promotions on sales and profits in the do-it-yourself market. Expert Syst. Appl. 27(1): 53-62 (2004) - [j5]Jedid-Jah Jonker, Nanda Piersma, Dirk Van den Poel
Joint optimization of customer segmentation and marketing policy to maximize long-term profitability. Expert Syst. Appl. 27(2): 159-168 (2004) - [j4]Bart Larivière
Investigating the role of product features in preventing customer churn, by using survival analysis and choice modeling: The case of financial services. Expert Syst. Appl. 27(2): 277-285 (2004) - 2002
- [j3]Bart Baesens, Stijn Viaene, Dirk Van den Poel
Bayesian neural network learning for repeat purchase modelling in direct marketing. Eur. J. Oper. Res. 138(1): 191-211 (2002) - [c3]Stijn Viaene, Bart Baesens, Guido Dedene, Jan Vanthienen, Dirk Van den Poel:
Proof Running Two State-Of-The-Art Pattern Recognition Techniques in the Field of Direct Marketing. ICEIS 2002: 446-454 - 2001
- [j2]Stijn Viaene, Bart Baesens, Tony Van Gestel, Johan A. K. Suykens
Knowledge discovery in a direct marketing case using least squares support vector machines. Int. J. Intell. Syst. 16(9): 1023-1036 (2001) - [j1]Stijn Viaene, Bart Baesens, Dirk Van den Poel
Wrapped input selection using multilayer perceptrons for repeat-purchase modeling in direct marketing. Intell. Syst. Account. Finance Manag. 10(2): 115-126 (2001) - 2000
- [c2]Stijn Viaene, Bart Baesens, Tony Van Gestel, Johan A. K. Suykens, Dirk Van den Poel
Knowledge Discovery Using Least Squares Support Vector Machine Classifiers: A Direct Marketing Case. PKDD 2000: 657-664
1990 – 1999
- 1998
- [c1]Dirk Van den Poel
Purchase Prediction in Database Marketing with the ProbRough System. Rough Sets and Current Trends in Computing 1998: 593-600
Coauthor Index
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