Adrien Ehrhardt, Christophe Biernacki, Vincent Vandewalle, Philippe Heinrich
arXiv 21 Mar 2019 · Statistics — Methodology · 1 citations (OpenAlex)
arXiv:1903.08920 · PDF · DOI · OpenAlex · Extracted main text
For regulatory and interpretability reasons, logistic regression is still widely used. To improve prediction accuracy and interpretability, a preprocessing step quantizing both continuous and categorical data is usually performed: continuous features are discretized and, if numerous, levels of categorical features are grouped. An even better predictive accuracy can be reached by embedding this quantization estimation step directly into the predictive estimation step itself. But doing so, the predictive loss has to be optimized on a huge set. To overcome this difficulty, we introduce a specific two-step optimization strategy: first, the optimization problem is relaxed by approximating discontinuous quantization functions by smooth functions; second, the resulting relaxed optimization problem is solved via a particular neural network. The good performances of this approach, which we call glmdisc, are illustrated on simulated and real data from the UCI library and Cr\'edit Agricole Consumer Finance (a major European historic player in the consumer credit market).
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chollet, F. et al (2015) Keras | 0.644 | 2 | 2 | 100% |
| 2 | Ramŕez-Gallego, S., Garcá, S., Mouriño-Talń, H., Martńez-Rego, D., B… (2016) Data discretization: taxonomy and big data challenge | 0.644 | 2 | 2 | 100% |
| 3 | Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C.,… (2015) TensorFlow: Large-scale machine learning on heterogeneous systems, 2015 | 0.644 | 2 | 2 | 100% |
| 4 | Schwarz, G (1978) Estimating the dimension of a model | 0.405 | 1 | 1 | 100% |
| 5 | Dheeru, D. and Karra Taniskidou, E (2017) UCI machine learning repository, 2017 | 0.405 | 1 | 1 | 100% |
| 6 | Akaike, H (1973) Information theory and an extension of the maximum likelihood principle | 0.405 | 1 | 1 | 100% |
| 7 | Anand, R., Mehrotra, K. G., Mohan, C. K., and Ranka, S (1993) An improved algorithm for neural network classification of imbalanced training sets | 0.405 | 1 | 1 | 100% |
| 8 | Berry, W. D., DeMeritt, J. H., and Esarey, J (2010) Testing for interaction in binary logit and probit models: Is a product term essential? | 0.405 | 1 | 1 | 100% |
| 9 | Bottou, L (2010) Large-scale machine learning with stochastic gradient descent | 0.405 | 1 | 1 | 100% |
| 10 | Chamroukhi, F., Samé, A., Govaert, G., and Aknin, P (2009) A regression model with a hidden logistic process for feature extraction from time series | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 21 scored citations.