Sandra Benítez-Peña

Sandra Benítez-Peña
Universidad de Sevilla | US · Statistics and Operations Research

Master of Science

About

11
Publications
4,947
Reads
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93
Citations
Citations since 2017
11 Research Items
92 Citations
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2017201820192020202120222023051015202530
2017201820192020202120222023051015202530
Education
September 2015 - July 2016
Universidad de Sevilla
Field of study
  • Mathematics
September 2011 - July 2015
Universidad de Sevilla
Field of study
  • Mathematics

Publications

Publications (11)
Preprint
Full-text available
In modelling the relative performance of a set of Decision Making Units (DMUs), a common challenge is to account for heterogeneity in the services they provide and the settings in which they operate. One solution is to include many features in the model and hereby to use a one-fits-all model that is sufficiently complex to account for this heteroge...
Article
Full-text available
Since the seminal paper by Bates and Granger in 1969, a vast number of ensemble methods that combine different base regressors to generate a unique one have been proposed in the literature. The so-obtained regressor method may have better accuracy than its components, but at the same time it may overfit, it may be distorted by base regressors with...
Preprint
Full-text available
Since the seminal paper by Bates and Granger in 1969, a vast number of ensemble methods that combine different base regressors to generate a unique one have been proposed in the literature. The so-obtained regressor method may have better accuracy than its components , but at the same time it may overfit, it may be distorted by base regressors with...
Preprint
Full-text available
Support vector machines (SVMs) are widely used and constitute one of the best examined and used machine learning models for 2-class classification. Classification in SVM is based on a score procedure, yielding a deterministic classification rule, which can be transformed into a probabilistic rule (as implemented in off-the-shelf SVM libraries), but...
Preprint
Full-text available
COVID-19 is an infectious disease that was first identified in China in December 2019. Subsequently COVID-19 started to spread broadly, to also arrive in Spain by the end of Jan-uary 2020. This pandemic triggered confinement measures, in order to reduce the expansion of the virus so as not to saturate the health care system. With the aim of providi...
Article
Full-text available
This paper proposes an integrative approach to feature (input and output) selection in Data Envelopment Analysis (DEA). The DEA model is enriched with zero-one decision variables modelling the selection of features, yielding a Mixed Integer Linear Programming formulation. This single-model approach can handle different objective functions as well a...
Preprint
Full-text available
This paper proposes an integrative approach to feature (input and output) selection in Data Envelopment Analysis (DEA). The DEA model is enriched with zero-one decision variables modelling the selection of features, yielding a Mixed Integer Linear Programming formulation. This single-model approach can handle different objective functions as well a...
Article
Full-text available
Support vector machine (SVM) is a powerful tool in binary classification, known to attain excellent misclassification rates. On the other hand, many realworld classification problems, such as those found in medical diagnosis, churn or fraud prediction, involve misclassification costs which may be different in the different classes. However, it may...
Article
Feature Selection is a crucial procedure in Data Science tasks such as Classification, since it identifies the relevant variables, making thus the classification procedures more interpretable, cheaper in terms of measurement and more effective by reducing noise and data overfit. The relevance of features in a classification procedure is linked to t...

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Project (1)
Project
NeEDS (Network of European Data Scientists) provides an integrated modelling and computing environment that facilitates data analysis and data visualization to enhance interaction. NeEDS brings together an excellent interdisciplinary research team that integrates expertise from three relevant academic disciplines, Mathematical Optimization, Visualization and Network Science, and is excellently placed to tackle the challenges. NeEDS develops mathematical models, yielding results which are interpretable, easy-to-visualize, and flexible enough to incorporate user knowledge from complex data. These models require the numerical resolution of computationally demanding Mixed Integer Nonlinear Programming formulations, and for this purpose NeEDS develops innovative mathematical optimization based heuristics.