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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Optimizing the class information divergence for transductive classification of texts using propagation in bipartite graphs

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Autor(es):
Faleiros, Thiago de Paulo ; Rossi, Rafael Geraldeli ; Lopes, Alneu de Andrade
Número total de Autores: 3
Tipo de documento: Artigo Científico
Fonte: PATTERN RECOGNITION LETTERS; v. 87, n. SI, p. 127-138, FEB 1 2017.
Citações Web of Science: 0
Resumo

Transductive classification is an useful way to classify a collection of unlabeled textual documents when only a small fraction of this collection can be manually labeled. Graph-based algorithms have aroused considerable interests in recent years to perform transductive classification since the graph-based representation facilitates label propagation through the graph edges. In a bipartite graph representation, nodes represent objects of two types, here documents and terms, and the edges between documents and terms represent the occurrences of the terms in the documents. In this context, the label propagation is performed from documents to terms and then from terms to documents iteratively. In this paper we propose a new graph-based transductive algorithm that use the bipartite graph structure to associate the available class information of labeled documents and then propagate these class information to assign labels for unlabeled documents. By associating the class information to edges linking documents to terms we guarantee that a single term can propagate different class information to its distinct neighbors. We also demonstrated that the proposed method surpasses the algorithms for transductive classification based on vector space model or graphs when only a small number of labeled documents is available. (C) 2016 Elsevier B.V. All rights reserved. (AU)

Processo FAPESP: 11/22749-8 - Desafios em visualização exploratória de dados multidimensionais: novos paradigmas, escalabilidade e aplicações
Beneficiário:Luis Gustavo Nonato
Linha de fomento: Auxílio à Pesquisa - Temático
Processo FAPESP: 15/14228-9 - Análise e Mineração de Redes Sociais
Beneficiário:Alneu de Andrade Lopes
Linha de fomento: Auxílio à Pesquisa - Regular
Processo FAPESP: 11/12823-6 - Extraindo padrões de coleções de documentos textuais utilizando redes heterogêneas
Beneficiário:Rafael Geraldeli Rossi
Linha de fomento: Bolsas no Brasil - Doutorado