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2. Interpretable generalized additive neural networks. (2024). Weinzierl, Sven ; Zschech, Patrick ; Kraus, Mathias ; Tschernutter, Daniel.
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3. Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling. (2021). de Caigny, Arno ; de Bock, Koen W.
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4. Fusing Vantage Point Trees and Linear Discriminants for Fast Feature Classification. (2017). Neves, Joo C ; Proena, Hugo.
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5. An asymptotically optimal kernel combined classifier. (2016). Mojirsheibani, Majid ; Kong, Jiajie.
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6. An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market. (2016). Fitzpatrick, Trevor ; Mues, Christophe.
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7. An ensemble of -nearest neighbours algorithm for detection of Parkinsons disease. (2015). Gk, Murat.
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8. A simple method for combining estimates to improve the overall error rates in classification. (2015). Balakrishnan, Narayanaswamy ; Mojirsheibani, Majid.
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9. A multi-loss super regression learner (MSRL) with application to survival prediction using proteomics. (2014). Datta, Susmita ; Shah, Jasmit .
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10. Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning. (2013). De Bock, Koen ; Coussement, Kristof.
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11. Reconciling Performance and Interpretability in Customer Churn Prediction using Ensemble Learning based on Generalized Additive Models. (2012). Van den Poel, Dirk ; De Bock, Koen.
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12. Consistency of support vector machines using additive kernels for additive models. (2012). Hable, Robert ; Christmann, Andreas.
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    RePEc:eee:csdana:v:56:y:2012:i:4:p:854-873.

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13. An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction. (2011). Van den Poel, Dirk ; De Bock, Koen.
    In: Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium.
    RePEc:rug:rugwps:11/717.

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14. Ensemble classification of paired data. (2011). Adler, Werner ; Potapov, Sergej ; Schmid, Matthias ; Lausen, Berthold ; Brenning, Alexander.
    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:55:y:2011:i:5:p:1933-1941.

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1. Teleworking and housing demand. (2023). Wersing, Martin ; Watson, Verity ; Schulz, Rainer.
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2. Enhancing discrete choice models with representation learning. (2020). Lurkin, Virginie ; Sifringer, Brian ; Alahi, Alexandre.
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3. Arriving at a decision: A semi-parametric approach to institutional birth choice in India. (2019). Daziano, Ricardo A ; Bansal, Prateek ; Sunder, Naveen.
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4. Flexible estimation of time-varying effects for frequently purchased retail goods: a modeling approach based on household panel data. (2018). Steiner, Winfried J ; Kneib, Thomas ; Guhl, Daniel ; Baumgartner, Bernhard.
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5. Estimating time-varying parameters in brand choice models: A semiparametric approach. (2018). Steiner, Winfried J ; Kneib, Thomas ; Guhl, Daniel ; Baumgartner, Bernhard.
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6. A Monte Carlo Evaluation of the Logit-Mixed Logit under Asymmetry and Multimodality. (2017). Scarpa, Riccardo ; Franceschinis, Cristiano ; Scarpan, Riccardo ; Thiene, Mara.
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7. Probabilistic forecasting with discrete choice models: Evaluating predictions with pseudo-coefficients of determination. (2016). Sung, Ming-Chien.
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8. Kernel-based semiparametric multinomial logit modelling of political party preferences. (2014). Sperlich, Stefan ; Langrock, Roland ; Heidenreich, Nils-Bastian .
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9. Generalized Additive Models for Nonmarket Valuation via Revealed or Stated Preference Methods. (2012). Ferrini, Silvia ; Fezzi, Carlo.
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10. Inference on finite population categorical response: nonparametric regression-based predictive approach. (2012). Banerjee, Tathagata ; Adhya, Sumanta ; Chattopadhyay, Gaurangadeb.
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11. Data augmentation, frequentist estimation, and the Bayesian analysis of multinomial logit models. (2011). Scott, Steven .
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12. Semiparametric specification of the utility function in a travel mode choice model. (2010). Fukuda, Daisuke ; Yai, Tetsuo.
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13. Ensemble classification based on generalized additive models. (2010). Van den Poel, Dirk ; De Bock, Koen ; Coussement, Kristof.
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14. Ensemble classification based on generalized additive models. (2010). Van den Poel, Dirk ; De Bock, Koen ; Coussement, Kristof.
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15. Using a heterogeneous multinomial probit model with a neural net extension to model brand choice. (2007). Hruschka, Harald.
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16. Generalized structured additive regression based on Bayesian P-splines. (2006). Lang, Stefan ; Brezger, Andreas.
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17. Allocation of catalogs to collective customers based on semiparametric response models. (2005). Hruschka, Harald ; Baumgartner, Bernhard.
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18. A new integrated model of noncompensatory and compensatory decision strategies. (2004). Elrod, Terry ; Johnson, Richard D. ; White, Joan.
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19. An empirical comparison of the validity of a neural net based multinomial logit choice model to alternative model specifications. (2004). Fettes, Werner ; Probst, Markus ; Hruschka, Harald.
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20. Investigating the Competitive Assumption of Multinomial Logit Models of Brand Choice by Nonparametric Modeling. (2003). Hildebrandt, Lutz ; Boztug, Yasemin ; Abe, Makoto.
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21. Investigation of the stochastic utility maximization process of consumer brand choice by semiparametric modeling. (2000). Abe, Makoto ; Hildebrandt, Lutz ; Boztuag, Yasemin .
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22. A nonparametric multiple choice method within the random utility framework. (2000). Huang, J u-Chin, ; Nychka, Douglas W..
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