Please use this identifier to cite or link to this item: http://repositorio.ufes.br/handle/10/4309
Title: Evaluating loss minimization in multi-label classification via stochastic simulation using beta distribution
metadata.dc.creator: MELLO, L. H. S.
Keywords: multi-label classification;loss minimization;data mining
Issue Date: 20-May-2016
Publisher: Universidade Federal do Espírito Santo
Citation: MELLO, L. H. S., Evaluating loss minimization in multi-label classification via stochastic simulation using beta distribution
Abstract: 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
Appears in Collections:PPGI - Dissertações de mestrado

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