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Evaluating loss minimization in multi-label classification via stochastic simulation using beta distribution

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Previous issue date: 2016-05-20 / The objective of this work is to present the effectiveness and efficiency of algorithms for solving the loss minimization problem in Multi-Label Classification (MLC). We first prove that a specific case of loss minimization in MLC isNP-complete for the loss functions Coverage and Search Length, and therefore,no efficient algorithm for solving such problems exists unless P=NP. Furthermore, we show a novel approach for evaluating multi-label algorithms that has the advantage of not being limited to some chosen base learners, such as K-neareast Neighbor and Support Vector Machine, by simulating the distribution of labels according to multiple Beta Distributions.

Identiferoai:union.ndltd.org:IBICT/oai:dspace2.ufes.br:10/4309
Date20 May 2016
CreatorsMELLO, L. H. S.
ContributorsRODRIGUES, A. L., Rauber, T. W., CARVALHO, A. P., Varejão, F. M.
PublisherUniversidade Federal do Espírito Santo, Mestrado em Informática, Programa de Pós-Graduação em Informática, UFES, BR
Source SetsIBICT Brazilian ETDs
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis
Formattext
Sourcereponame:Repositório Institucional da UFES, instname:Universidade Federal do Espírito Santo, instacron:UFES
Rightsinfo:eu-repo/semantics/openAccess

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