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Published in:
2018 | OriginalPaper | Chapter
Coupling the PAELLA Algorithm to Predictive Models
Authors : Manuel Castejón-Limas, Hector Alaiz-Moreton, Laura Fernández-Robles, Javier Alfonso-Cendón, Camino Fernández-Llamas, Lidia Sánchez-González, Hilde Pérez
Published in: International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding
Publisher: Springer International Publishing
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Abstract
This paper explores the benefit of using the PAELLA algorithm in an innovative way. The PAELLA algorithm was originally developed in the context of outlier detection and data cleaning. As a consequence, it is usually seen as a discriminant tool that categorizes observations into two groups: core observations and outliers. A new look at the information contained in its output provides ample opportunity in the context of data driven predictive models. The information contained in the occurrence vector is used through the experiments reported in a quest for finding how to take advantage of that information. The results obtained in each successive experiment guide the researcher to a sensible use case in which this information proves extremely useful: probabilistic sampling regression.