Use este identificador para citar ou linkar para este item: http://repositorio.ufes.br/handle/10/4309
Título: Evaluating loss minimization in multi-label classification via stochastic simulation using beta distribution
Autor(es): Mello, Lucas Henrique Sousa
Orientador: Varejão, Flávio Miguel
Data do documento: 20-Mai-2016
Editor: Universidade Federal do Espírito Santo
Resumo: 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.
URI: http://repositorio.ufes.br/handle/10/4309
Aparece nas coleções:PPGI - Dissertações de mestrado

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