Publications

@article{castejon-limas_robust_2020,

title = {Robust weighted regression via PAELLA sample weights},

author = {Manuel Castejón-Limas and Hector Alaiz-Moreton and Laura Fernández-Robles and Javier Alfonso-Cendón and Camino Fernández-Llamas and Lidia Sánchez-González and Hilde Pérez},

url = {https://www.sciencedirect.com/science/article/pii/S0925231219316066},

year  = {2020},

date = {2020-01-01},

journal = {Neurocomputing},

volume = {391},

pages = {325–333},

abstract = {This paper reports the usage of the occurrence vector provided by the PAELLA algorithm in the context of robust regression. PAELLA was originally conceived as an outlier detection and data cleaning technique. A novel approach is to use this algorithm not for discarding outliers but to generate information related to the reliability of the observations recorded in the dataset. This approach proves to provide successful results when compared to traditional common practice such as outlier removal. A set of experiments using a contrived difficult artificial dataset are described using both neural networks and classical polynomial fitting. Finally, a successful comparison of our approach to two state-of-the-art algorithms proves the benefits of using the PAELLA algorithm in the context of robust regression.},

note = {Publisher: Elsevier},

keywords = {Multilayer Perceptron, Outlier Detection, PAELLA, Robust Regression, weighted regression},

pubstate = {published},

tppubtype = {article}

}

@article{castejon_limas_non-removal_2020,

title = {Non-removal strategy for outliers in predictive models: The PAELLA algorithm case},

author = {Manuel Castejón Limas and Hector Alaiz Moreton and Laura Fernández Robles and Javier Alfonso Cendón and Camino Fernández Llamas and Lidia Sánchez González and Hilde Pérez},

url = {https://academic.oup.com/jigpal/article-abstract/28/4/418/5670471?login=true},

year  = {2020},

date = {2020-01-01},

journal = {Logic Journal of the IGPL},

volume = {28},

number = {4},

pages = {418–429},

abstract = {his paper proposes an innovative use of the PAELLA algorithm, originally designed for outlier detection, in robust regression. It leverages the occurrence vector from the algorithm to strengthen the influence of reliable samples and reduce the impact of outliers. Several experiments were conducted to assess the use of this vector, comparing different approaches and showing that using weighted neural networks outperforms traditional methods.},

note = {Publisher: Oxford University Press},

keywords = {neural networks, Outlier Detection, PAELLA, Robust Regression},

pubstate = {published},

tppubtype = {article}

}

@article{castejon-limas_coupling_2018,

title = {Coupling the paella algorithm to predictive models},

author = {Manuel Castejón-Limas and Hector Alaiz-Moreton and Laura Fernández-Robles and Javier Alfonso-Cendón and Camino Fernández-Llamas and Lidia Sánchez-González and Hilde Pérez},

url = {https://link.springer.com/chapter/10.1007/978-3-319-67180-2_49},

year  = {2018},

date = {2018-01-01},

journal = {International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding 12},

pages = {505–512},

abstract = {This paper investigates a novel use of the PAELLA algorithm, originally designed for outlier detection and data cleaning. Traditionally seen as a discriminant tool, its output provides valuable insights for data-driven predictive models. By leveraging the occurrence vector, experiments explore its potential, ultimately identifying a key application: probabilistic sampling regression.},

note = {Publisher: Springer International Publishing},

keywords = {Outlier Detection, PAELLA, Probalistic Sampling},

pubstate = {published},

tppubtype = {article}

}