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}

}