Correlated binary data for machine learning
Visualitza/Obre
10.23919/EUSIPCO54536.2021.9616346
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/360381
Tipus de documentText en actes de congrés
Data publicació2021
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condicions d'accésAccés obert
Tots els drets reservats. Aquesta obra està protegida pels drets de propietat intel·lectual i
industrial corresponents. Sense perjudici de les exempcions legals existents, queda prohibida la seva
reproducció, distribució, comunicació pública o transformació sense l'autorització del titular dels drets
ProjecteTECNOLOGIAS RADIO PARA COMUNICACIONES UBICUAS EN LA EVOLUCION DE 5G A 6G (AEI-PID2019-104945GB-I00)
Abstract
Data sets containing instances that are assigned values by an ensemble of annotators of unknown accuracy are becoming increasingly common. Binary, potentially correlated data are frequent in a number of disciplines, and thus eligible to be exploited by ensemble meta-learners. A prior key step is testing the meta-learners with synthetic data sets featuring realistic correlation patterns, which is the main scope of this work. To achieve this goal, two challenges are faced: (i) finding out a new correlated pattern to model Bernoulli random variables, and (ii) obtaining a process to generate realistic synthetic data sets. A comparative analysis and performance results are provided for two methods of artificial data generation. The methods are also tested using two state-of-the-art binary ensemble meta-learners that consider inter-classifier dependencies.
CitacióLlobet, M.; Cabrera-Bean, M. Correlated binary data for machine learning. A: European Signal Processing Conference. "2021 29th European Signal Processing Conference (EUSIPCO)". Institute of Electrical and Electronics Engineers (IEEE), 2021, p. 1411-1415. ISBN 978-9-0827-9706-0. DOI 10.23919/EUSIPCO54536.2021.9616346.
ISBN978-9-0827-9706-0
Versió de l'editorhttps://ieeexplore.ieee.org/document/9616346
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EUSIPCO_reviewed.pdf | 527,7Kb | Visualitza/Obre |