Publications

@article{alaiz-rodriguez_class_2011,

title = {Class and subclass probability re-estimation to adapt a classifier in the presence of concept drift},

author = {Rocío Alaiz-Rodríguez and Alicia Guerrero-Curieses and Jesús Cid-Sueiro},

url = {https://www.sciencedirect.com/science/article/pii/S0925231211002359},

year  = {2011},

date = {2011-01-01},

journal = {Neurocomputing},

volume = {74},

number = {16},

pages = {2614–2623},

abstract = {This work proposes an algorithm to adapt classifiers when training and test data have different distributions. Unlike methods that adjust only class priors, it models changes at the subclass level without retraining. Experiments show better adaptation to new conditions.},

note = {Publisher: Elsevier},

keywords = {Changing Operational Conditions, Concept Drift, Imprecise Class Distribution, Imprecise Data Distribution, neural networks, Posterior Probability Estimation, Supervised Classification},

pubstate = {published},

tppubtype = {article}

}

@article{santos-rodriguez_cost-sensitive_2009,

title = {Cost-sensitive learning based on Bregman divergences},

author = {Raúl Santos-Rodríguez and Alicia Guerrero-Curieses and Rocío Alaiz-Rodríguez and Jesús Cid-Sueiro},

url = {https://link.springer.com/content/pdf/10.1007/s10994-009-5132-8.pdf},

year  = {2009},

date = {2009-01-01},

journal = {Machine Learning},

volume = {76},

pages = {271–285},

abstract = {This paper explores using a specific class of Bregman divergences to design cost-sensitive classifiers for multiclass problems. These divergences improve posterior probability estimation near decision boundaries. Asymptotically, they minimize decision costs in non-separable problems and maximize margins in separable MAP problems.},

note = {Publisher: Springer US},

keywords = {Bregman Divergences, Cost-sensitive Classification, Decision Boundary Optimization, Multiclass Classification, Posterior Probability Estimation},

pubstate = {published},

tppubtype = {article}

}

@article{guerrero-curieses_cost-sensitive_2009,

title = {Cost-sensitive and modular land-cover classification based on posterior probability estimates},

author = {A Guerrero-Curieses and Rocío Alaiz-Rodríguez and J Cid-Sueiro},

url = {https://www.tandfonline.com/doi/abs/10.1080/01431160902787695},

year  = {2009},

date = {2009-01-01},

journal = {International Journal of Remote Sensing},

volume = {30},

number = {22},

pages = {5877–5899},

abstract = {This paper proposes a modular approach for land-cover classification from satellite images, focusing learning on the decision boundary using posterior probability estimates. A self-configuring architecture addresses class conflicts, and the learning algorithm prioritizes critical probability regions based on user-defined misclassification costs. Additionally, filtering the posterior probability map helps reduce impulsive noise common in automatic classification. Experiments on multi- and hyperspectral images demonstrate the method’s effectiveness compared to approaches like Support Vector Machines.},

note = {Publisher: Taylor & Francis},

keywords = {Decision Boundary Learning, Hyperspectral Imaging, Land-cover Classification, Posterior Probability Estimation, Satellite Imagery},

pubstate = {published},

tppubtype = {article}

}