Coupling the PAELLA Algorithm to Predictive Models

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 649)


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.


Probabilistic sampling Outlier detection 



We gratefully acknowledge the financial support of Spanish Ministerio de Economía, Industria y Competitividad through grant DPI2016-79960-C3-2-P. We would like to also express our gratitude to Castilla y León Supercomputing Center whose cooperation allowed us to run around one million neural network trainings for the experiments reported on this paper.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.University of LeónLeonSpain

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