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Dirk Van den Poel
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2020 – today
- 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
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