D. V. Poel

发表

Technology classification with latent semantic indexing

Dirk Thorleuchter, Dirk Van den Poel, D. Thorleuchter, 2013, Expert Syst. Appl..

Faculteit Economie En Bedrijfskunde Hoveniersberg 24 B-9000 Gent Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-selection Techniques Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparin

Kristof Coussement, Dirk Van den Poel, D. V. Poel, 2022 .

Predicting customer retention and profitability by using random forests and regression forests techniques

Dirk Van den Poel, Bart Larivière, D. V. Poel, 2005, Expert Syst. Appl..

Dynamic cross-sales effects of price promotions: Empirical generalizations

D. V. Poel, G. Wets, B. Vindevogel, 2004 .

Direct and Indirect Effects of Retail Promotions

Jan De Schamphelaere, D. V. Poel, G. Wets, 2003 .

Predicting customer loyalty using the internal transactional database

Dirk Van den Poel, Wouter Buckinx, Geert Verstraeten, 2007, Expert Syst. Appl..

Predicting home-appliance acquisition sequences: Markov/Markov for Discrimination and survival analysis for modeling sequential information in NPTB models

Dirk Van den Poel, Anita Prinzie, D. V. Poel, 2007, Decis. Support Syst..

The Status of Energy Price Modelling and its Relevance to Marketing in Emerging Economies

D. V. Poel, I. Soares, G. Sisodia, 2015 .

Consumer Acceptance of the Internet as a Channel of Distribution

D. V. Poel, Joseph Leunis, 1999 .

An extended Huff-model for robustly benchmarking and predicting retail network performance

D. V. Poel, N. V. D. Weghe, M. D. Beule, 2014 .

Binary quantile regression: a Bayesian approach based on the asymmetric Laplace distribution

Dirk Van den Poel, Dries F. Benoit, D. Benoit, 2012 .

Perceived risk and rise reduction strategies in mail-order versus retail store buying

Dirk Van den Poel, D. V. Poel, Joseph Leunis, 1996 .

Outlier‐Robust Bayesian Multinomial Choice Modeling

Dries F. Benoit, D. Benoit, D. V. Poel, 2016 .

Does the Use of Solar and Wind Energy Increase Retail Prices in Europe? Evidence from EU-27☆

D. V. Poel, Paula F. V. Ferreira, I. Soares, 2015 .

The Value of Neighborhood Information in Prospect Selection Models: Investigating the Optimal Level of Granularity

D. V. Poel, Philippe Baecke, 2013 .

WORKING PAPER Kernel Factory: An Ensemble of Kernel Machines

D. V. Poel, Michel Ballings, 2013 .

Thiophene Complexes on Montmorillonite saturated with Different Cations

D. V. Poel, J. Camerlynck, P. Cloos, 1973 .

Including the Salesperson Effect in Purchasing Behavior Models Using PROC GLIMMIX

D. V. Poel, Philippe Baecke, 2012 .

Predicting cross-selling opportunities using a sequential architecture.

Dirk Van den Poel, Anita Prinzie, Patrick Van Kenhove, 2002 .

Box office sales and social media: A cross-platform comparison of predictive ability and mechanisms

Asil Oztekin, Michel Ballings, Dirk Van den Poel, 2021, Decis. Support Syst..

Mail-order versus retail store buying - The role of perceived risk and risk reduction strategies

Dirk Van den Poel, D. V. Poel, Joseph Leunis, 1992 .

Adding interpretability to predictive maintenance by machine learning on sensor data

Dirk Van den Poel, Bram Steurtewagen, D. V. Poel, 2021, Comput. Chem. Eng..

Modeling partial customer churn: On the value of first product-category purchase sequences

João Falcão e Cunha, Dirk Van den Poel, Vera L. Miguéis, 2012, Expert systems with applications.

Social media optimization: Identifying an optimal strategy for increasing network size on Facebook

Michel Ballings, Dirk Van den Poel, Matthias Bogaert, 2016 .

The impact of price, branding and money-back guarantee on store choice

D. V. Poel, Joseph Leunis, 1995 .

A Survey on Churn Prediction Techniques in Communication Sector

A. Keramati, D. V. Poel, M. Kechadi, 2016 .

Predicting customer profitability during acquisition: Finding the optimal combination of data source and data mining technique

Dirk Thorleuchter, Dirk Van den Poel, Jeroen D'Haen, 2013, Expert Syst. Appl..

Price rigidity in Europe and the US: A comparative analysis using scanner data

D. V. Poel, Benjamin Verhelst, 2010 .

bayesQR: Bayesian quantile regression

Rahim Alhamzawi, Dirk Van den Poel, Dries F. Benoit, 2011 .

Enhanced decision support in credit scoring using Bayesian binary quantile regression

Dirk Van den Poel, Dries F. Benoit, Vera L. Miguéis, 2013, J. Oper. Res. Soc..

WORKING PAPER Binary quantile regression: A Bayesian approach based on the asymmetric Laplace density

Dirk Van den Poel, Dries F. Benoit, D. Benoit, 2010 .

RFM Variables Revisited Using Quantile Regression

Michel Ballings, Dirk Van den Poel, Dries Benoit, 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

Empathy as added value in predicting donation behavior

Dirk Van den Poel, D. V. Poel, Griet Verhaert, 2011 .

Evaluating the Added Value of Pictorial Data for Customer Churn Prediction

Michel Ballings, Dirk Van den Poel, E. Verhagen, 2013 .

Improving Customer Churn Prediction by Data Augmentation Using Pictorial Stimulus-Choice Data

Michel Ballings, Dirk Van den Poel, Emmanuel Verhagen, 2012, IS-MiS.

Improved marketing decision making in a customer churn prediction context using generalized additive models

Kristof Coussement, Dirk Van den Poel, Dries F. Benoit, 2010, Expert Syst. Appl..

Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting

Dirk Van den Poel, Wouter Buckinx, D. V. Poel, 2005, Eur. J. Oper. Res..

Customer attrition analysis for financial services using proportional hazard models

Dirk Van den Poel, Bart Larivière, D. V. Poel, 2004, Eur. J. Oper. Res..

Using machine learning techniques to predict defection of top clients

Bart Baesens, Jan Vanthienen, Wouter Buckinx, 2002 .

Knowledge discovery in a direct marketing case using least squares support vector machines

Johan A. K. Suykens, Bart De Moor, Bart Baesens, 2001, Int. J. Intell. Syst..

An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction

Koen W. De Bock, Dirk Van den Poel, D. V. Poel, 2011, Expert Syst. Appl..

Identifying Soccer Players on Facebook Through Predictive Analytics

Michel Ballings, Dirk Van den Poel, Matthias Bogaert, 2017, Decis. Anal..

Predicting Self-declared Movie Watching Behavior Using Facebook Data and Information-Fusion Sensitivity Analysis

Michel Ballings, Dirk Van den Poel, Matthias Bogaert, 2019, Decis. Sci..

Evaluating the importance of different communication types in romantic tie prediction on social media

Michel Ballings, Dirk Van den Poel, Matthias Bogaert, 2016, Annals of Operations Research.

The added value of Facebook friends data in event attendance prediction

Michel Ballings, Dirk Van den Poel, Matthias Bogaert, 2016, Decis. Support Syst..

Evaluating multiple classifiers for stock price direction prediction

Michel Ballings, Dirk Van den Poel, Nathalie Hespeels, 2015, Expert Syst. Appl..

CRM in social media: Predicting increases in Facebook usage frequency

Michel Ballings, Dirk Van den Poel, D. V. Poel, 2015, Eur. J. Oper. Res..

Handling class imbalance in customer churn prediction

Dirk Van den Poel, Jonathan Burez, D. V. Poel, 2009, Expert Syst. Appl..

Joint optimization of customer segmentation and marketing policy to maximize long-term profitability

Dirk Van den Poel, Nanda Piersma, Jedid-Jah Jonker, 2002, Expert Syst. Appl..

Incorporating sequential information into traditional classification models by using an element / position-sensitive SAM

Dirk Van den Poel, Anita Prinzie, D. V. Poel, 2005 .

Separating financial from commercial customer churn: A modeling step towards resolving the conflict between the sales and credit department

Dirk Van den Poel, Jonathan Burez, D. V. Poel, 2008, Expert Syst. Appl..

The added value of social media data in B2B customer acquisition systems: A real-life experiment

Michel Ballings, Dirk Van den Poel, Matthijs Meire, 2017, Decis. Support Syst..

Integrating expert knowledge and multilingual web crawling data in a lead qualification system

Dirk Thorleuchter, Dirk Van den Poel, Dries F. Benoit, 2016, Decis. Support Syst..

Leveraging sequential information from multivariate behavioral sensor data to predict the moment of calving in dairy cattle using deep learning

D. V. Poel, M. Hostens, F. V. Eerdenburg, 2021, Comput. Electron. Agric..

Neural network survival analysis for personal loan data

Bart Baesens, Jan Vanthienen, Dirk Van den Poel, 2005, J. Oper. Res. Soc..

Identifying new product ideas : waiting for the wisdom of the crowd or screening ideas in real time

Michel Ballings, Dirk Van den Poel, Edward C. Malthouse, 2017 .

Predicting e-commerce company success by mining the text of its publicly-accessible website

Dirk Thorleuchter, Dirk Van den Poel, D. Thorleuchter, 2012, Expert Syst. Appl..

Bayesian neural network learning for repeat purchase modelling in direct marketing

Bart Baesens, Jan Vanthienen, Guido Dedene, 2002, Eur. J. Oper. Res..

Why promotion strategies based on market basket analysis do not work

Geert Wets, Dirk Van den Poel, Bernd Vindevogel, 2005, Expert Syst. Appl..

Idea mining for web-based weak signal detection

Dirk Thorleuchter, D. Van den Poel, D. Thorleuchter, 2015 .

Semantic weak signal tracing

Dirk Thorleuchter, Dirk Van den Poel, Tobias Scheja, 2014, Expert Syst. Appl..

A Performance Analysis of Fault Recovery in Stream Processing Frameworks

Giselle van Dongen, Dirk Van Den Poel, D. V. Poel, 2021, IEEE Access.

Model-supported business-to-business prospect prediction based on an iterative customer acquisition framework

Dirk Van den Poel, Jeroen D'Haen, D. V. Poel, 2013 .

bayesQR: A Bayesian Approach to Quantile Regression

Dirk Van den Poel, Dries F. Benoit, D. Benoit, 2017 .

Modeling complex longitudinal consumer behavior with Dynamic Bayesian networks: an Acquisition Pattern Analysis application

Dirk Van den Poel, Anita Prinzie, D. V. Poel, 2011, Journal of Intelligent Information Systems.

Integrating the voice of customers through call center emails into a decision support system for churn prediction

Kristof Coussement, Dirk Van den Poel, D. V. Poel, 2008, Inf. Manag..

Improving Customer Complaint Management by Automatic Email Classification Using Linguistic Style Features as Predictors

Kristof Coussement, Dirk Van den Poel, D. V. Poel, 2007 .

Customer-adapted coupon targeting using feature selection

Geert Wets, Dirk Van den Poel, Wouter Buckinx, 2004, Expert Syst. Appl..

Manufacturer-retailer promotion competition: customisation of coupon target selection

Dirk Van den Poel, Wouter Buckinx, D. V. Poel, 2003 .

Faculteit Economie En Bedrijfskunde Hoveniersberg 24 B-9000 Gent Bayesian Kernel-based Classification for Financial Distress Detection Dirk Van Den Poel 4 Bayesian Kernel Based Classification for Financial Distress Detection

Johan A. K. Suykens, Bart Baesens, Dirk Van den Poel, 2022 .

Identification of interdisciplinary ideas

D. Van den Poel, D. Thorleuchter, D. Thorleuchter, 2016, Inf. Process. Manag..

Do Mobile Phone Data Provide a Better Denominator in Crime Rates and Improve Spatiotemporal Predictions of Crime?

Nico Van de Weghe, Thom Snaphaan, Wim Hardyns, 2021, ISPRS Int. J. Geo Inf..

CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services

Dirk Van den Poel, Jonathan Burez, D. V. Poel, 2007, Expert Syst. Appl..

FACULTEIT ECONOMIE

Dirk Van den Poel, Anita Prinzie, D. V. Poel, 2007 .

Investigating the post-complaint period by means of survival analysis

Dirk Van den Poel, Bart Larivière, D. V. Poel, 2005, Expert Syst. Appl..

Predicting online-purchasing behaviour

Dirk Van den Poel, Wouter Buckinx, D. V. Poel, 2005, Eur. J. Oper. Res..

Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models

Koen W. De Bock, Dirk Van den Poel, D. V. Poel, 2012, Expert Syst. Appl..

Incorporating sequential information in bankruptcy prediction with predictors based on Markov for discrimination

Dirk Van den Poel, Dries F. Benoit, Andrey Volkov, 2017, Decis. Support Syst..

The added value of auxiliary data in sentiment analysis of Facebook posts

Michel Ballings, Dirk Van den Poel, Matthijs Meire, 2016, Decis. Support Syst..

Protecting research and technology from espionage

Dirk Thorleuchter, Dirk Van den Poel, D. Thorleuchter, 2013, Expert Syst. Appl..

Faculteit Economie En Bedrijfskunde Hoveniersberg 24 B-9000 Gent Predicting Mail-order Repeat Buying: Which Variables Matter? Predicting Mail-order Repeat Buying: Which Variables Matter?

Dirk Van den Poel, D. V. Poel, Dirk Van den Poel, 2022 .

Banking behaviour after the lifecycle event of "moving in together": An exploratory study of the role of marketing investments

Dirk Van den Poel, Bart Larivière, D. V. Poel, 2007, Eur. J. Oper. Res..

Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers

Kristof Coussement, Dirk Van den Poel, D. V. Poel, 2009, Expert Syst. Appl..

Customer event history for churn prediction: How long is long enough?

Michel Ballings, Dirk Van den Poel, D. V. Poel, 2012, Expert Syst. Appl..

Investigating purchasing-sequence patterns for financial services using Markov, MTD and MTDg models

Dirk Van den Poel, Anita Prinzie, D. V. Poel, 2006, Eur. J. Oper. Res..

Using Webcrawling of Publicly Available Websites to Assess E-commerce Relationships

Dirk Thorleuchter, Dirk Van den Poel, D. Thorleuchter, 2012, 2012 Annual SRII Global Conference.

Temporary Staffing Services: A Data Mining Perspective

Dirk Van den Poel, Jeroen D'Haen, D. V. Poel, 2012, 2012 IEEE 12th International Conference on Data Mining Workshops.

Purchase Prediction in Database Marketing with the ProbRough System

Dirk Van den Poel, Zdzislaw Piasta, D. V. Poel, 1998, Rough Sets and Current Trends in Computing.

Rough Sets for Database Marketing

Dirk Van den Poel, D. V. Poel, 1998 .

Assessing the principles of spatial competition between stores within a retail network

Nico Van de Weghe, Dirk Van den Poel, Matthias De Beule, 2015 .

Weak signal identification with semantic web mining

Dirk Thorleuchter, Dirk Van den Poel, D. Thorleuchter, 2013, Expert Syst. Appl..

The impact of sample bias on consumer credit scoring performance and profitability

Dirk Van den Poel, Geert Verstraeten, D. V. Poel, 2005, J. Oper. Res. Soc..

Quantifying Credit-scoring Performance

Dirk Van den Poel, Geert Verstraeten, D. V. Poel, 2003 .

Equity price direction prediction for day trading: Ensemble classification using technical analysis indicators with interaction effects

Michel Ballings, Dirk Van den Poel, Celine Chesterman, 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

Dynamics between social media engagement, firm-generated content, and live and time-shifted TV viewing

Ewa Maslowska, Dirk Van den Poel, Edward C. Malthouse, 2018 .

Predicting the milk yield curve of dairy cows in the subsequent lactation period using deep learning

Dirk Van den Poel, Arno Liseune, Matthieu Salamone, 2021, Comput. Electron. Agric..

Faculteit Economie En Bedrijfskunde Hoveniersberg 24 B-9000 Gent Bayesian Network Classifiers for Identifying the Slope of the Customer Lifecycle of Long-life Customers Bayesian Network Classifiers for Identifying the Slope of the Customer Lifecycle of Long-life Customers

Bart Baesens, Jan Vanthienen, Michael Egmont-Petersen, 2022 .

The role of seed money and threshold size in optimizing fundraising campaigns: Past behavior matters!

Dirk Van den Poel, Griet Alice Verhaert, D. V. Poel, 2012, Expert Syst. Appl..

Ensemble classification based on generalized additive models

Kristof Coussement, Koen W. De Bock, Dirk Van den Poel, 2010, Comput. Stat. Data Anal..

Database marketing modelling for financial services using hazard rate models

Dirk Van den Poel, D. V. Poel, Joseph Leunis, 1998 .

A compared R&D-based and patent-based cross impact analysis for identifying relationships between technologies

Dirk Thorleuchter, D. Van den Poel, Anita Prinzie, 2010 .

Evaluating multi-label classifiers and recommender systems in the financial service sector

Michel Ballings, Dirk Van den Poel, Matthias Bogaert, 2019, Eur. J. Oper. Res..

Constrained optimization of data-mining problems to improve model performance: A direct-marketing application

Dirk Van den Poel, Anita Prinzie, D. V. Poel, 2005, Expert Syst. Appl..

Mining ideas from textual information

Dirk Thorleuchter, Dirk Van den Poel, Anita Prinzie, 2010, Expert Syst. Appl..

The Dangers of Using Intention as a Surrogate for Retention in Brand Positioning Decision Support Systems

Michel Ballings, Dirk Van den Poel, D. V. Poel, 2012, GfKl.

Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences

João Falcão e Cunha, Dirk Van den Poel, Ana S. Camanho, 2012, Adv. Data Anal. Classif..

Dynamic Bayesian Networks for Acquisition Pattern Analysis: A Financial-Services Cross-Sell Application

Dirk Van den Poel, Anita Prinzie, D. V. Poel, 2009, PAKDD Workshops.

Investigating Purchasing Patterns for Financial Services using Markov , MTD and MTDg Models

Dirk Van den Poel, Anita Prinzie, D. V. Poel, 2004 .

Detecting Sequential Patterns For Cross-selling Fast Moving Consumer Goods

Dirk Van den Poel, Anita Prinzie, Patrick Van Kenhove, 2003 .

Direct and indirect effects of retail promotions on sales and profits in the do-it-yourself market

Geert Wets, Dirk Van den Poel, Jan De Schamphelaere, 2003, Expert Syst. Appl..

Improved Emergency Management by a Loosely Coupled Logistic System

Dirk Thorleuchter, Dirk Van den Poel, Joachim Schulze, 2012, Future Security.

Preventing Customers from Running Away! Exploring Generalized Additive Models for Customer Churn Prediction

Kristof Coussement, Dirk Van den Poel, Dries F. Benoit, 2015 .

Improving customer acquisition models by incorporating spatial autocorrelation at different levels of granularity

Dirk Van den Poel, Philippe Baecke, D. V. Poel, 2012, Journal of Intelligent Information Systems.

Wrapped input selection using multilayer perceptrons for repeat-purchase modeling in direct marketing

Bart Baesens, Jan Vanthienen, Guido Dedene, 2001, Intell. Syst. Account. Finance Manag..

Knowledge Discovery Using Least Squares Support Vector Machine Classifiers: A Direct Marketing Case

Johan A. K. Suykens, Bart De Moor, Bart Baesens, 2000, PKDD.

Wrapped Feature Selection For BinaryClassification Bayesian Regularisation NeuralNetworks: A Database Marketing Application

Jan Vanthienen, Guido Dedene, Stijn Viaene, 2000 .

Proof Running Two State-Of-The-Art Pattern Recognition Techniques in the Field of Direct Marketing

Bart Baesens, Jan Vanthienen, Guido Dedene, 2002, ICEIS.

Latency Measurement of Fine-Grained Operations in Benchmarking Distributed Stream Processing Frameworks

Dirk Van den Poel, Bram Steurtewagen, Giselle van Dongen, 2018, 2018 IEEE International Congress on Big Data (BigData Congress).

Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting

D. V. Poel, W. Buckinx, 2005, Eur. J. Oper. Res..

Joint optimization of customer segmentation and marketing policy to maximize long-term profitability

D. V. Poel, N. Piersma, J. Jonker, 2002 .

Customer-adapted coupon targeting using feature selection

D. V. Poel, G. Wets, E. Moons, 2004, Expert Syst. Appl..

Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers

D. V. Poel, B. Baesens, M. Egmont-Petersen, 2004, Eur. J. Oper. Res..

Predicting Mail-Order Repeat Buying: Which Variables Matter?

D. V. Poel, M. Dekimpe, Joseph Leunis, 1999 .

Random Multiclass Classification: Generalizing Random Forests to Random MNL and Random NB

Dirk Van den Poel, Anita Prinzie, D. V. Poel, 2007, DEXA.

Benefits of quantile regression for the analysis of customer lifetime value in a contractual setting: An application in financial services

Dirk Van den Poel, Dries F. Benoit, D. V. Poel, 2009, Expert Syst. Appl..

Customer attrition analysis for financial services using proportional hazard models

D. V. Poel, Bart Larivière, 2004, Eur. J. Oper. Res..

Predicting donation behavior: Acquisition modeling in the nonprofit sector using Facebook data

Dirk Van den Poel, Matthias Bogaert, Lisa Schetgen, 2020, Decis. Support Syst..

Kernel Factory: An ensemble of kernel machines

Michel Ballings, Dirk Van den Poel, D. V. Poel, 2013, Expert Syst. Appl..

Random Forests for multiclass classification: Random MultiNomial Logit

D. V. Poel, A. Prinzie, 2008, Expert Syst. Appl..

Leveraging latent representations for milk yield prediction and interpolation using deep learning

Dirk Van den Poel, Arno Liseune, Matthieu Salamone, 2020, Comput. Electron. Agric..

Bayesian kernel based classification for financial distress detection

J. Suykens, D. V. Poel, B. Baesens, 2006, Eur. J. Oper. Res..

Investigating the post-complaint period by means of survival analysis

D. V. Poel, Bart Larivière, 2005, Expert Syst. Appl..

Customer attrition analysis for financial services using proportional hazard models

D. V. Poel, Bart Larivière, 2004 .

Analyzing Website Content for Improved R&T Collaboration Planning

Dirk Thorleuchter, Dirk Van den Poel, D. Thorleuchter, 2013, WorldCIST.

Joint optimization of customer segmentation and marketing policy to maximize long-term profitability

D. V. Poel, N. Piersma, J. Jonker, 2002, Expert Syst. Appl..

Faculteit Economie En Bedrijfskunde Hoveniersberg 24 B-9000 Gent Investigating Purchasing Patterns for Financial Services Using Markov, Mtd and Mtdg Models Investigating Purchasing Patterns for Financial Services Using Markov, Mtd and Mtdg Models

D. V. Poel, A. Prinzie, Tel, 2022 .

Machine learning refinery sensor data to predict catalyst saturation levels

Dirk Van den Poel, Bram Steurtewagen, D. V. Poel, 2020, Comput. Chem. Eng..

Extracting Information from Sequences of Financial Ratios with Markov for Discrimination: An Application to Bankruptcy Prediction

Dirk Van den Poel, Andrey Volkov, D. V. Poel, 2012, 2012 IEEE 12th International Conference on Data Mining Workshops.

Including spatial interdependence in customer acquisition models: A cross-category comparison

Dirk Van den Poel, Philippe Baecke, D. V. Poel, 2012, Expert Syst. Appl..

Improving customer retention in financial services using kinship network information

Dirk Van den Poel, Dries F. Benoit, D. V. Poel, 2012, Expert Syst. Appl..

Integrating the voice of customers through call center emails into a decision support system for churn prediction

D. V. Poel, Kristof Coussement, 2008, Inf. Manag..

The Role of Marketer-Generated Content in Customer Engagement Marketing

D. V. Poel, V. Kumar, K. Hewett, 2019, Journal of Marketing.

Investigating the post-complaint period by means of survival analysis

D. V. Poel, Bart Larivière, 2005, Expert Syst. Appl..

Improved Emergency Management by a Loosely Coupled Logistic System

D. Thorleuchter, D. V. Poel, J. Schulze, 2012, Future Security.

Analyzing existing customers' websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing

Dirk Thorleuchter, Dirk Van den Poel, Anita Prinzie, 2012, Expert Syst. Appl..

Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques

D. V. Poel, Kristof Coussement, 2008, Expert Syst. Appl..

Improving Purchasing Behavior Predictions by Data Augmentation with Situational Variables

Dirk Van den Poel, Philippe Baecke, D. V. Poel, 2010, Int. J. Inf. Technol. Decis. Mak..

Web mining based extraction of problem solution ideas

Dirk Thorleuchter, Dirk Van den Poel, D. Thorleuchter, 2013, Expert Syst. Appl..

Improved multilevel security with latent semantic indexing

Dirk Thorleuchter, Dirk Van den Poel, D. Thorleuchter, 2012, Expert Syst. Appl..

Incorporating sequential information into traditional classification models by using an element/position-sensitive SAM

D. V. Poel, A. Prinzie, Dirk Van den Poel, 2006, Decis. Support Syst..

Predicting online-purchasing behaviour

D. V. Poel, W. Buckinx, 2005, Eur. J. Oper. Res..

Using Eye-Tracking Data of Advertisement Viewing Behavior to Predict Customer Churn

Michel Ballings, Dirk Van den Poel, D. V. Poel, 2013, 2013 IEEE 13th International Conference on Data Mining Workshops.