Publications

@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}

}