Publications

@article{castejon-limas_robust_2020,

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

author = {Manuel Castejón-Limas and Héctor Alaiz-Moretón 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{alaiz-moreton_ground-level_2019,

title = {Ground-level ozone predictions using outlier identification leveraged sample weighted regressors},

author = {Héctor Alaiz-Moretón and Laura Fernández-Robles and Javier Alfonso-Cendón and Manuel Castejón-Limas and Lidia Sánchez-González and Hilde Pérez},

url = {https://www.tandfonline.com/doi/abs/10.1080/0952813X.2018.1509898},

year  = {2019},

date = {2019-01-01},

journal = {Journal of Experimental & Theoretical Artificial Intelligence},

volume = {31},

number = {6},

pages = {829–840},

abstract = {This paper proposes a new method for predicting ground-level ozone concentrations by addressing raw data without preprocessing, specifically by weighting the impact of automatically detected outliers. The method was tested against traditional outlier removal techniques in Ponferrada, Spain, and showed great performance in both simple and sophisticated regression models like linear regression and multi-layer perceptron algorithms.},

note = {Publisher: Taylor & Francis},

keywords = {Ground-Level Ozone, Outlier Detection, Ozone Prediction, weighted regression},

pubstate = {published},

tppubtype = {article}

}

@article{castejon-limas_paella_2018,

title = {PAELLA as a Booster in Weighted Regression},

author = {Manuel Castejón-Limas and Héctor Alaiz-Moretón 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_25},

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 = {259–265},

abstract = {This paper reports the use of the PAELLA algorithm in the context of weighted regression. First, an experiment comparing this new approach versus probabilistic macro sampling is reported, as a natural extension of previous work. Then another different experiment is reported where this approach is tested against a state of the art regression technique. Both experiments provide satisfactory results.},

note = {Publisher: Springer International Publishing},

keywords = {Outlier Detection, weighted regression},

pubstate = {published},

tppubtype = {article}

}