Publications

@article{moreno-torres_unifying_2012,

title = {A unifying view on dataset shift in classification},

author = {José G Moreno-Torres and Troy Raeder and Rocío Alaiz-Rodríguez and Nitesh V Chawla and Francisco Herrera},

url = {https://www.sciencedirect.com/science/article/pii/S0031320311002901},

year  = {2012},

date = {2012-01-01},

journal = {Pattern recognition},

volume = {45},

number = {1},

pages = {521–530},

abstract = {The field of dataset shift has received a growing amount of interest in the last few years. The fact that most real-world applications have to cope with some form of shift makes its study highly relevant. The literature on the topic is mostly scattered, and different authors use different names to refer to the same concepts, or use the same name for different concepts. With this work, we attempt to present a unifying framework through the review and comparison of some of the most important works in the literature.},

note = {Publisher: Pergamon},

keywords = {data distribution, data science, dataset shift, machine learning, research framework},

pubstate = {published},

tppubtype = {article}

}

@article{alaiz-rodriguez_improving_2009,

title = {Improving classification under changes in class and within-class distributions},

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

url = {https://link.springer.com/chapter/10.1007/978-3-642-02478-8_16},

year  = {2009},

date = {2009-01-01},

journal = {International Work-Conference on Artificial Neural Networks},

pages = {122–130},

abstract = {This paper introduces a re-estimation algorithm that adapts classifiers to changing data distributions by using unlabeled operational data. It assumes that classes consist of unknown subclasses and that subclass probabilities may change after training. The method improves performance in scenarios where subclass probabilities change, while maintaining similar results when they don’t.},

note = {Publisher: Springer Berlin Heidelberg Berlin, Heidelberg},

keywords = {Classifier Adaptation, data distribution, Subclass Probabilities},

pubstate = {published},

tppubtype = {article}

}