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{castejon-limas_non-removal_2020,

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

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

year  = {2020},

date = {2020-01-01},

urldate = {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{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{alaiz-moreton_data_2018,

title = {Data mining techniques for the estimation of variables in health-related noisy data},

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://link.springer.com/chapter/10.1007/978-3-319-67180-2_47},

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 = {482–491},

abstract = {This paper presents a new approach to predicting ozone levels using raw data and an outlier detection technique, aiming to achieve reliable results without the need for preprocessing. The method was tested using experimental data from Ponferrada, Spain, and demonstrated satisfactory outcomes, even in challenging cases. This approach addresses the need for accurate ozone level predictions to combat pollution’s impact on public health, particularly in developed countries.},

note = {Publisher: Springer International Publishing},

keywords = {Air Pollution, Outlier Detection, Ozone Prediction, Public Health},

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}

}

@article{castejon-limas_coupling_2018,

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

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_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}

}