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}

}