default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDDirk Van den Poel
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j88]Bram Janssens
, Matthias Bogaert, Astrid Bagué, Dirk Van den Poel:
B2Boost: instance-dependent profit-driven modelling of B2B churn. Ann. Oper. Res. 341(1): 267-293 (2024)- [j87]Bram Janssens, Lisa Schetgen, Matthias Bogaert, Matthijs Meire, Dirk Van den Poel:
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, Dirk Van den Poel:
A Performance Analysis of Fault Recovery in Stream Processing Frameworks. IEEE Access 9: 93745-93763 (2021) - [j85]Giselle van Dongen, Dirk Van den Poel:
Influencing Factors in the Scalability of Distributed Stream Processing Jobs. IEEE Access 9: 109413-109431 (2021) - [j84]Bram Steurtewagen, Dirk Van den Poel:
Adding interpretability to predictive maintenance by machine learning on sensor data. Comput. Chem. Eng. 152: 107381 (2021) - [j83]Arno Liseune, Matthieu Salamone, Dirk Van den Poel, Bonifacius Van Ranst, Miel Hostens:
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, Rob Bergmans, Dirk Van den Poel:
Predicting Self-declared Movie Watching Behavior Using Facebook Data and Information-Fusion Sensitivity Analysis. Decis. Sci. 52(3): 776-810 (2021) - [j80]Lisa Schetgen, Matthias Bogaert, Dirk Van den Poel:
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, Asil Oztekin:
Box office sales and social media: A cross-platform comparison of predictive ability and mechanisms. Decis. Support Syst. 147: 113517 (2021) - [j78]Anneleen Rummens, Thom Snaphaan, Nico Van de Weghe, Dirk Van den Poel, Lieven J. R. Pauwels, Wim Hardyns:
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, Dirk Van den Poel:
Machine learning refinery sensor data to predict catalyst saturation levels. Comput. Chem. Eng. 134: 106722 (2020) - [j76]Arno Liseune, Matthieu Salamone, Dirk Van den Poel, Bonifacius Van Ranst, Miel Hostens:
Leveraging latent representations for milk yield prediction and interpolation using deep learning. Comput. Electron. Agric. 175: 105600 (2020) - [j75]Giselle van Dongen, Dirk Van den Poel:
Evaluation of Stream Processing Frameworks. IEEE Trans. Parallel Distributed Syst. 31(8): 1845-1858 (2020)
2010 – 2019
- 2019
- [j74]Matthias Bogaert, Justine Lootens, Dirk Van den Poel, Michel Ballings:
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, Bram Steurtewagen, Dirk Van den Poel:
Latency Measurement of Fine-Grained Operations in Benchmarking Distributed Stream Processing Frameworks. BigData Congress 2018: 247-250 - 2017
- [j72]Matthias Bogaert, Michel Ballings, Martijn Hosten, Dirk Van den Poel:
Identifying Soccer Players on Facebook Through Predictive Analytics. Decis. Anal. 14(4): 274-297 (2017) - [j71]Andrey Volkov, Dries F. Benoit, Dirk Van den Poel:
Incorporating sequential information in bankruptcy prediction with predictors based on Markov for discrimination. Decis. Support Syst. 98: 59-68 (2017) - [j70]Matthijs Meire, Michel Ballings, Dirk Van den Poel:
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, Dirk Thorleuchter, Dries F. Benoit:
Integrating expert knowledge and multilingual web crawling data in a lead qualification system. Decis. Support Syst. 82: 69-78 (2016) - [j67]Matthijs Meire, Michel Ballings, Dirk Van den Poel:
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, Celine Chesterman, Maxim Koppen, Michel Ballings:
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, Nathalie Hespeels, Ruben Gryp:
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, Dirk Van den Poel:
Quantitative cross impact analysis with latent semantic indexing. Expert Syst. Appl. 41(2): 406-411 (2014) - [j61]Dirk Thorleuchter, Dirk Van den Poel:
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, Dirk Van den Poel:
Technology classification with latent semantic indexing. Expert Syst. Appl. 40(5): 1786-1795 (2013) - [j58]Jeroen D'Haen, Dirk Van den Poel, Dirk Thorleuchter:
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, Dirk Van den Poel:
Kernel Factory: An ensemble of kernel machines. Expert Syst. Appl. 40(8): 2904-2913 (2013) - [j56]Dirk Thorleuchter, Dirk Van den Poel:
Protecting research and technology from espionage. Expert Syst. Appl. 40(9): 3432-3440 (2013) - [j55]Dirk Thorleuchter, Dirk Van den Poel:
Web mining based extraction of problem solution ideas. Expert Syst. Appl. 40(10): 3961-3969 (2013) - [j54]Dirk Thorleuchter, Dirk Van den Poel:
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, Dries F. Benoit, Dirk Van den Poel:
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, Dirk Van den Poel:
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, Dirk Van den Poel, Ana S. Camanho, João Falcão e Cunha:
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, Dirk Van den Poel, Anita Prinzie:
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, Dirk Van den Poel:
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, Dirk Van den Poel, Ana S. Camanho, João Falcão e Cunha:
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, Dirk Van den Poel:
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, Dirk Van den Poel:
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, Dirk Van den Poel:
Improved multilevel security with latent semantic indexing. Expert Syst. Appl. 39(18): 13462-13471 (2012) - [j41]Michel Ballings, Dirk Van den Poel:
Customer event history for churn prediction: How long is long enough? Expert Syst. Appl. 39(18): 13517-13522 (2012) - [c18]Dirk Thorleuchter, Joachim Schulze, Dirk Van den Poel:
Improved Emergency Management by a Loosely Coupled Logistic System. Future Security 2012: 5-8 - [c17]Dirk Thorleuchter, Dirk Van den Poel:
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, Emmanuel Verhagen:
Improving Customer Churn Prediction by Data Augmentation Using Pictorial Stimulus-Choice Data. IS-MiS 2012: 217-226 - [c12]Dirk Thorleuchter, Dirk Van den Poel:
Using Webcrawling of Publicly Available Websites to Assess E-commerce Relationships. SRII Global Conference 2012: 402-410 - 2011
- [j40]Koen W. De Bock, Dirk Van den Poel:
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, Dries F. Benoit, Dirk Van den Poel:
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, Kristof Coussement, Dirk Van den Poel:
Ensemble classification based on generalized additive models. Comput. Stat. Data Anal. 54(6): 1535-1546 (2010) - [j36]Kristof Coussement, Dries F. Benoit, Dirk Van den Poel:
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, Dirk Van den Poel, Anita Prinzie:
Mining ideas from textual information. Expert Syst. Appl. 37(10): 7182-7188 (2010) - [j34]Koen W. De Bock, Dirk Van den Poel:
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, Dirk Van den Poel:
Ensembles of Probability Estimation Trees for Customer Churn Prediction. IEA/AIE (2) 2010: 57-66 - [c8]Dirk Thorleuchter, Dirk Van den Poel, Anita Prinzie:
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, Dirk Van den Poel:
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, Dirk Van den Poel:
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, Dirk Van den Poel:
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, Dirk Van den Poel:
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, Dirk Van den Poel:
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, Dirk Van den Poel:
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, Dirk Van den Poel, Dirk-Emma Baestaens, Marleen Willekens:
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, Wouter Buckinx:
Predicting online-purchasing behaviour. Eur. J. Oper. Res. 166(2): 557-575 (2005) - [j15]Bernd Vindevogel, Dirk Van den Poel, Geert Wets:
Why promotion strategies based on market basket analysis do not work. Expert Syst. Appl. 28(3): 583-590 (2005) - [j14]Bart Larivière, Dirk Van den Poel:
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, Dirk Van den Poel:
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, Jan Vanthienen:
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, Michael Egmont-Petersen, Patrick Van Kenhove, Jan Vanthienen:
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, Bart Larivière:
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, Geert Wets:
Customer-adapted coupon targeting using feature selection. Expert Syst. Appl. 26(4): 509-518 (2004) - [j6]Dirk Van den Poel, Jan De Schamphelaere, Geert Wets:
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, Dirk Van den Poel:
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, Jan Vanthienen, Guido Dedene:
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, Dirk Van den Poel, Jan Vanthienen, Bart De Moor, Guido Dedene:
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, Guido Dedene, Jan Vanthienen:
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, Jan Vanthienen, Bart De Moor, Guido Dedene:
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, Zdzislaw Piasta:
Purchase Prediction in Database Marketing with the ProbRough System. Rough Sets and Current Trends in Computing 1998: 593-600
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-23 20:30 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
