- Agarwal, R. ; Melnick, L. ; Frosst, N. ; Zhang, X. ; Lengerich, B. ; Caruana, R. ; Hinton, G.E. Neural additive models: Interpretable machine learning with neural nets. 2021 Curran Associates, Inc:
Paper not yet in RePEc: Add citation now
- Al-Ebbini, L. ; Oztekin, A. ; Sevkli, Z. ; Delen, D. Predicting quality of life for lung transplant recipients: A hybrid genetic algorithms-based methodology. 2017 IEEE:
Paper not yet in RePEc: Add citation now
- Angwin, J. ; Larson, J. ; Mattu, S. ; Kirchner, L. Machine bias. 2016 Auerbach Publications:
Paper not yet in RePEc: Add citation now
- Badirli, S., Liu, X., Xing, Z., Bhowmik, A., Doan, K., & Keerthi, S. S. Gradient boosting neural networks: GrowNet. arXiv:2002.07971[cs, stat].
Paper not yet in RePEc: Add citation now
- Barredo Arrieta, A. ; Díaz-Rodríguez, N. ; Del Ser, J. ; Bennetot, A. ; Tabik, S. ; Barbado, A. ; Herrera, F. Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. 2020 Information Fusion. 58 82-115
Paper not yet in RePEc: Add citation now
Bastos, J.A. ; Matos, S.M. Explainable models of credit losses. 2022 European Journal of Operational Research. 301 386-394
Borchert, P. ; Coussement, K. ; De Caigny, A. ; De Weerdt, J. Extending business failure prediction models with textual website content using deep learning. 2023 European Journal of Operational Research. 306 348-357
Carbonneau, R. ; Laframboise, K. ; Vahidov, R. Application of machine learning techniques for supply chain demand forecasting. 2008 European Journal of Operational Research. 184 1140-1154
- Caruana, R. ; Lou, Y. ; Gehrke, J. ; Koch, P. ; Sturm, M. ; Elhadad, N. Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. 2015 :
Paper not yet in RePEc: Add citation now
- Chang, C.-H. ; Tan, S. ; Lengerich, B. ; Goldenberg, A. ; Caruana, R. How interpretable and trustworthy are GAMs?. 2021 ACM: Virtual Event Singapore
Paper not yet in RePEc: Add citation now
- Chen, C. ; Lin, K. ; Rudin, C. ; Shaposhnik, Y. ; Wang, S. ; Wang, T. A holistic approach to interpretability in financial lending: Models, visualizations, and summary-explanations. 2022 Decision Support Systems. 152 113647-
Paper not yet in RePEc: Add citation now
- Chen, T. ; Guestrin, C. Xgboost: A scalable tree boosting system. 2016 :
Paper not yet in RePEc: Add citation now
Chou, P. ; Chuang, H.H.-C. ; Chou, Y.-C. ; Liang, T.-P. Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning. 2022 European Journal of Operational Research. 296 635-651
Ciocan, D.F. ; Mišić, V.V. Interpretable optimal stopping. 2022 Management Science. 68 1616-1638
- Coussement, K. ; Benoit, D.F. Interpretable data science for decision making. 2021 Decision Support Systems. 150 113664-
Paper not yet in RePEc: Add citation now
Coussement, K. ; Benoit, D.F. ; Van den Poel, D. Improved marketing decision making in a customer churn prediction context using generalized additive models. 2010 Expert Systems with Applications. 37 2132-2143
- Coussement, K. ; Benoit, D.F. ; Van den Poel, D. Preventing customers from running away! Exploring generalized additive models for customer churn prediction. 2015 Springer International Publishing: Cham
Paper not yet in RePEc: Add citation now
De Bock, K.W. ; Coussement, K. ; Van den Poel, D. Ensemble classification based on generalized additive models. 2010 Computational Statistics and Data Analysis. 54 1535-1546
De Caigny, A. ; Coussement, K. ; De Bock, K.W. A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. 2018 European Journal of Operational Research. 269 760-772
- Demšar, J. Statistical comparisons of classifiers over multiple data sets. 2006 The Journal of Machine Learning Research. 7 1-30
Paper not yet in RePEc: Add citation now
- Du, M. ; Liu, N. ; Hu, X. Techniques for interpretable machine learning. 2019 Communications of the ACM. 63 68-77
Paper not yet in RePEc: Add citation now
Dumitrescu, E. ; Hué, S. ; Hurlin, C. ; Tokpavi, S. Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects. 2022 European Journal of Operational Research. 297 1178-1192
- FICO (2018). Explainable machine learning challenge. https://community.fico.com/s/explainable-machine-learning-challenge.
Paper not yet in RePEc: Add citation now
- Friedman, J.H. ; Stuetzle, W. Projection pursuit regression. 1981 Journal of the American Statistical Association. 76 817-823
Paper not yet in RePEc: Add citation now
- García, S. ; Fernández, A. ; Luengo, J. ; Herrera, F. Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. 2010 Information Sciences. 180 2044-2064
Paper not yet in RePEc: Add citation now
- Hastie, T. ; Tibshirani, R. Generalized additive models. 1986 Statistical Science. 1 -
Paper not yet in RePEc: Add citation now
- Huang, G.-B. ; Zhu, Q.-Y. ; Siew, C.-K. Extreme learning machine: Theory and applications. 2006 Neurocomputing. 70 489-501
Paper not yet in RePEc: Add citation now
- Imran, A.A. ; Amin, M.N. ; Islam Rifat, M.R. ; Mehreen, S. Deep neural network approach for predicting the productivity of garment employees. 2019 :
Paper not yet in RePEc: Add citation now
Janiesch, C. ; Zschech, P. ; Heinrich, K. Machine learning and deep learning. 2021 Electronic Markets. 31 685-695
Kraus, M. ; Feuerriegel, S. ; Oztekin, A. Deep learning in business analytics and operations research: Models, applications and managerial implications. 2020 European Journal of Operational Research. 281 628-641
- Lou, Y. ; Caruana, R. ; Gehrke, J. Intelligible models for classification and regression. 2012 :
Paper not yet in RePEc: Add citation now
- Lou, Y. ; Caruana, R. ; Gehrke, J. ; Hooker, G. Accurate intelligible models with pairwise interactions. 2013 ACM: Chicago Illinois USA
Paper not yet in RePEc: Add citation now
- Lundberg, S.M. ; Erion, G. ; Chen, H. ; DeGrave, A. ; Prutkin, J.M. ; Nair, B. ; Lee, S.-I. From local explanations to global understanding with explainable AI for trees. 2020 Nature Machine Intelligence. 2 56-67
Paper not yet in RePEc: Add citation now
- Maldonado, S. ; Vairetti, C. ; Fernandez, A. ; Herrera, F. FW-SMOTE: A feature-weighted oversampling approach for imbalanced classification. 2022 Pattern Recognition. 124 108511-
Paper not yet in RePEc: Add citation now
- Martens, D. Data science ethics: Concepts, techniques, and cautionary tales. 2022 Oxford University Press:
Paper not yet in RePEc: Add citation now
- Miller, T. Explanation in artificial intelligence: Insights from the social sciences. 2019 Artificial Intelligence. 267 1-38
Paper not yet in RePEc: Add citation now
Mitrović, S. ; Baesens, B. ; Lemahieu, W. ; De Weerdt, J. On the operational efficiency of different feature types for telco churn prediction. 2018 European Journal of Operational Research. 267 1141-1155
- Molnar, C. ; Casalicchio, G. ; Bischl, B. Interpretable machine learning - a brief history, state-of-the-art and challenges. 2020 En : Koprinska, I. ECML PKDD 2020 workshops communications in computer and information science. Springer International Publishing: Cham
Paper not yet in RePEc: Add citation now
- Neumann, K. ; Steil, J.J. Batch intrinsic plasticity for extreme learning machines. 2011 Springer:
Paper not yet in RePEc: Add citation now
- Nori, H., Jenkins, S., Koch, P., & Caruana, R. Interpretml: A unified framework for machine learning interpretability. arXiv:1909.09223.
Paper not yet in RePEc: Add citation now
- Piri, S. ; Delen, D. ; Liu, T. ; Zolbanin, H.M. A data analytics approach to building a clinical decision support system for diabetic retinopathy: Developing and deploying a model ensemble. 2017 Decision Support Systems. 101 12-27
Paper not yet in RePEc: Add citation now
- Rao, A., & Greenstein, B. (2022). PwC 2022 AI business survey. https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-business-survey.html.
Paper not yet in RePEc: Add citation now
- Reddy, B.K. ; Delen, D. ; Agrawal, R.K. Predicting and explaining inflammation in Crohn’s disease patients using predictive analytics methods and electronic medical record data. 2019 Health Informatics Journal. 25 1201-1218
Paper not yet in RePEc: Add citation now
- Ribeiro, M.T. ; Singh, S. ; Guestrin, C. “Why should I trust you?”: Explaining the predictions of any classifier. 2016 ACM Press: San Francisco, California, USA
Paper not yet in RePEc: Add citation now
- Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. 2019 Nature Machine Intelligence. 1 206-215
Paper not yet in RePEc: Add citation now
- Rudin, C. ; Radin, J. Why are we using black box models in AI when we don’t need to? A lesson from an explainable AI competition. 2019 Harvard Data Science Review. 1 -
Paper not yet in RePEc: Add citation now
- Schaul, T. ; Zhang, S. ; LeCun, Y. No more pesky learning rates. 2013 :
Paper not yet in RePEc: Add citation now
Senoner, J. ; Netland, T. ; Feuerriegel, S. Using explainable artificial intelligence to improve process quality: Evidence from semiconductor manufacturing. 2022 Management Science. 68 5704-5723
- Vaughan, J., Sudjianto, A., Brahimi, E., Chen, J., & Nair, V. N. Explainable neural networks based on additive index models. arXiv:1806.01933.
Paper not yet in RePEc: Add citation now
- Wang, C. ; Han, B. ; Patel, B. ; Rudin, C. In pursuit of interpretable, fair and accurate machine learning for criminal recidivism prediction. 2023 Journal of Quantitative Criminology. 39 519-581
Paper not yet in RePEc: Add citation now
- Xu, Z., Dai, A. M., Kemp, J., & Metz, L. Learning an adaptive learning rate schedule. arXiv preprint arXiv:1909.09712.
Paper not yet in RePEc: Add citation now
- Yang, Z. ; Zhang, A. ; Sudjianto, A. Enhancing explainability of neural networks through architecture constraints. 2021 IEEE Transactions on Neural Networks and Learning Systems. 32 2610-2621
Paper not yet in RePEc: Add citation now
- Yang, Z. ; Zhang, A. ; Sudjianto, A. GAMI-net: An explainable neural network based on generalized additive models with structured interactions. 2021 Pattern Recognition. 120 108192-
Paper not yet in RePEc: Add citation now
- Zschech, P. ; Weinzierl, S. ; Hambauer, N. ; Zilker, S. ; Kraus, M. GAM(e) change or not? An evaluation of interpretable machine learning models based on additive model constraints. 2022 :
Paper not yet in RePEc: Add citation now