Counterfactual analysis and target setting in benchmarking
Peter Bogetoft, Jasone Ramírez-Ayerbe, Dolores Romero Morales (2024), European Journal of Operational Research, DOI: 10.1016/j.ejor.2024.01.005; Published online: 2024-01-08
Keywords:
Mathematical optimization modelling for group counterfactual explanations
Emilio Carrizosa, Jasone Ramírez-Ayerbe, Dolores Romero Morales (2024), European Journal of Operational Research, DOI: 10.1016/j.ejor.2024.01.002; Published online: 2024-01-05
Keywords: Machine learning, Interpretability, Mathematical optimization, Counterfactual explanations, Location analysis
CATCHM: A novel network-based credit card fraud detection method using node representation learning
Rafaël Van Belle, Bart Baesens, Jochen De Weerdt (2023), Decision Support Systems, DOI: 10.1016/j.dss.2022.113866; Published online: 2022-09-22
Keywords: Network representation learning, DeepWalk, Credit card fraud, Fraud detection
Simulation-based optimization of user interfaces for quality-assuring machine learning model predictions
Y. Zhang, M. Tennekes, T. De Jong, L. Curier, B. Coecke, and M. Chen (2024), ACM Transactions on Interactive Intelligent Systems, DOI: 10.1145/3594552; Published online: 2023-05-23
Keywords: model-based evaluation, quality assurance, interactive machine learning, data labeling, classification
Radial Icicle Tree (RIT): Node separation and area constancy
Y. Jin, T. J. A. de Jong, M. Tennekes, and M. Chen (2024), IEEE Transactions on Visualization and Computer Graphics, DOI: 10.1109/TVCG.2023.3327178; Published online: 2023-10-26
Keywords: Tree visualization, icicle tree, sunburst tree, size encoding, area constancy, node separation, radial icicle tree, RIT
Supervised feature compression based on counterfactual analysis
V. Piccialli, D. Romero Morales, C. Salvatore (2024), European Journal of Operational Research, DOI: 10.1016/j.ejor.2023.11.019; Published online: 2023-11-15.
Keywords: Machine learning, Supervised classification, Interpretability, Feature compression, Counterfactual analysis
Generating collective counterfactual explanations in score-based classification via mathematical optimization
Emilio Carrizosa, Jasone Ramírez-Ayerbe, Dolores Romero Morales (2024), Expert Systems with Applications, DOI: 10.1016/j.eswa.2023.121954; Published online: 2023-10-13.
Keywords: Machine Learning, Classification model, Mathematical Optimization, Optimization Problem
A new model for counterfactual analysis for functional data
Emilio Carrizosa, Jasone Ramírez-Ayerbe, Dolores Romero Morales
(2023), Advances in Data Analysis and Classification, DOI: 10.1007/s11634-023-00563-5 ; Published online: 2023-10-25.
Keywords: Counterfactual explanations, Mathematical optimization, Functional data, Prototypes, Random forests
Cost-sensitive probabilistic predictions for support vector machines
Sandra Benítez-Peña, Rafael Blanquero, Emilio Carrizosa, Pepa Ramírez-Cobo
(2023), European Journal of Operational Research, DOI: 10.1016/j.ejor.2023.09.027; Published online: 2023-10-23.
Keywords: Machine Learning, Support Vector Machines, Probabilistic Classification, Cost-Sensitive Classification
On clustering and interpreting with rules by means of mathematical optimization
Emilio Carrizosa, Kseniia Kurishchenko, Alfredo Marín, Dolores Romero Morales
(2023), Computers & Operations Research, DOI: 10.1016/j.cor.2023.106180; Published online: 2023-03-02.
Keywords: Machine learning, Interpretability, Cluster analysis, Rules, Mixed-integer programming
On optimal regression trees to detect critical intervals for multivariate functional data
Rafael Blanquero, Emilio Carrizosa, Cristina Molero-Río, Dolores Romero Morales
(2023), Computers & Operations Research, DOI: 10.1016/j.cor.2023.106152; Published online: 2023-01-13.
Keywords: Optimal randomized regression trees, Multivariate functional data, Critical intervals detection, Nonlinear programming
A bounded measure for estimating the benefit of visualization (Part I): theoretical discourse and conceptual evaluation
M. Chen and M. Sbert
(2022), Entropy, DOI: 10.3390/e24020228; Published online: 2022-01-31.
Keywords: information theory, theory of visualization, cost–benefit analysis, divergence measure, benefit of visualization, human knowledge in visualization, abstraction, deformation, volume visualization, metro map
Predicting student performance using sequence classification with time-based windows
Galina Deeva, Johannes De Smedt, Cecilia Saint-Pierre, Richard Weber, Jochen De Weerdt
(2022), Expert Systems with Applications, DOI: 10.1016/j.eswa.2022.118182; Published online: 2022-12-15.
Keywords: Machine learning, Sequence mining, Feature engineering, Success prediction, Behavioral patterns
On mathematical optimization for clustering categories in contingency tables
Emilio Carrizosa, Vanesa Guerrero, Dolores Romero Morales
(2022), Advances in Data Analysis and Classification, DOI: 10.1007/s11634-022-00508-4; Published online: 2022-06-28.
Keywords: Contingency tables, Mathematical optimization, Relational constraints, Clustering
The tree based linear regression model for hierarchical categorical variables
Emilio Carrizosa, Laust Hvas Mortensen, Dolores Romero Morales, M. Remedios Sillero-Denamiel
(2022), Expert Systems with Applications, DOI: 10.1016/j.eswa.2022.117423; Published online: 2022-10-01.
Keywords: Hierarchical categorical variables, Linear regression models, Accuracy vs. model complexity, Mixed integer convex quadratic problem with linear constraints
On sparse optimal regression trees
Rafael Blanquero, Emilio Carrizosa, Cristina Molero-Río, Dolores Romero Morales.
(2021), European Journal of Operational Research, DOI: 10.1016/j.ejor.2021.12.022; Published online: 2021-12-18.
Keywords: Machine Learning, Classification and regression trees, Optimal regression trees, Sparsity, Nonlinear Programming.
Relevant open-source routines can be found here
Interpreting clusters via prototype optimization
Emilio Carrizosa, Kseniia Kurishchenko, Alfredo Marín, Dolores Romero Morales.
(2021), Omega, DOI: 10.1016/j.omega.2021.102543; Published online: 2021-09-23.
Keywords: Machine Learning, Interpretability, Cluster Analysis, Prototypes, Mixed-Integer Programming.
Constrained Naïve Bayes with application to unbalanced data classification
Rafael Blanquero, Emilio Carrizosa, Pepa Ramírez Cobo, M Remedios Sillero-Denamiel.
(2021), Central European Journal of Operations Research, DOI: 10.1007/s10100-021-00782-1; Published online: 2021-10-20.
Keywords: Probabilistic Classification, Constrained optimization, Parameter estimation, Efficiency measures, Naïve Bayes.
Variable selection for Naïve Bayes classification
Rafael Blanquero, Emilio Carrizosa, Pepa Ramírez Cobo, M Remedios Sillero-Denamiel.
(2021), Computers & Operations Research, DOI: 10.1016/j.cor.2021.105456; Published online: 2021-06-02.
Keywords: Clustering, Conditional Independence, Dependence measures, Heuristics, Probabilistic Classification, Cost-sensitive Classification.
On Clustering Categories of Categorical Predictors in Generalized Linear Models
Emilio Carrizosa, Marcela Galvis Restrepo, Dolores Romero Morales.(2021), Expert Systems with Applications, DOI: 10.1016/j.eswa.2021.115245; Published online: 2021-05-24.
Keywords: Statistical Learning, Interpretability, Greedy Randomized Adaptive Search Procedure, Proximity between categories.
On sparse ensemble methods: An application to short-term predictions of the evolution of COVID-19
Sandra Benítez-Peña, Emilio Carrizosa, Vanesa Guerrero., M Dolores Jiménez-Gamero, Belén Martín-Barragán, Cristina Molero-Río, Pepa Ramírez-Cobo, Dolores Romero Morales, M Remedios Sillero-Denamiel. (2021), European Journal of Operational Research, DOI: 10.1016/j.ejor.2021.04.016; Published online: 2021-04-18.
Keywords: Machine Learning, Ensemble Method, Mathematical Optimization, Selective Sparsity, COVID-19.
Mathematical optimization in classification and regression trees
Emilio Carrizosa, Cristina Molero-Río, Dolores Romero Morales.(2021), TOP, DOI: 10.1007/s11750-021-00594-1; Published online: 2021-03-17.
Keywords: Classification and regression trees, Tree ensembles, Mixed-integer linear optimization, Continuous nonlinear optimization, Sparsity, Explainability.
Optimal randomized classification trees
Rafael Blanquero, Emilio Carrizosa, Cristina Molero-Río, Dolores Romero Morales.(2021), Computers & Operations Research, DOI: 10.1016/j.cor.2021.105281; Published online: 2021-03-08.
Keywords: Classification and regression trees, Cost-sensitive classification, Nonlinear programming.
A cost-sensitive constrained Lasso
Rafael Blanquero, Emilio Carrizosa, Pepa Ramírez-Cobo, M. Remedios Sillero-Denamiel.(2020), Advances in Data Analysis and Classification, DOI: 10.1007/s11634-020-00389-5; Published online: 2020-03-12.
Keywords: Performance constraints, Cost-sensitive learning, Sparse solutions, Sample average approximation, Heterogeneity, Lasso.
Expert-driven trace clustering with instance-level constraints
Pieter De Koninck, Klaas Nelissen, Seppe vanden Broucke, Bart Baesens, Monique Snoeck, Jochen De Weerdt(2020), Knowledge and Information Systems, DOI: 10.1007/s10115-021-01548-6; Published online: 2020-03-01.
Keywords: Trace clustering, Process mining, Semi-supervised learning, Constrained clustering.
Relevant open-source software can be found here and here
Sparsity in Optimal Randomized Classification Trees
Rafael Blanquero, Emilio Carrizosa , Cristina Molero-Río, Dolores Romero Morales(2019), European Journal of Operational Research, Elsevier Ltd., DOI: 10.1016/j.ejor.2019.12.002; Published online: 2019-12-16
Keywords: Data mining, Optimal Classification Trees, Global and Local Sparsity, Nonlinear Programming.
Feature Selection in Data Envelopment Analysis: A Mathematical Optimization approach