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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Multi-label semi-supervised classification through optimum-path forest

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Amorim, Willian P. [1] ; Falcao, Alexandre X. [2] ; Papa, Joao P. [3]
Total Authors: 3
[1] Univ Fed Mato Grosso do Sul, Inst Comp, Campo Grande, MS - Brazil
[2] Univ Estadual Campinas, Inst Comp, Campinas, SP - Brazil
[3] Sao Paulo State Univ, Dept Comp, Bauru, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: INFORMATION SCIENCES; v. 465, p. 86-104, OCT 2018.
Web of Science Citations: 4

Multi-label classification consists of assigning one or multiple classes to each sample in a given dataset. However, the project of a multi-label classifier is usually limited to a small number of supervised samples as compared to the number of all possible label combinations. This scenario favors semi-supervised learning methods, which can cope with the absence of supervised samples by adding unsupervised ones to the training set. Recently, we proposed a semi-supervised learning method based on optimum connectivity for single-label classification. In this work, we extend it for multi-label classification with considerable effectiveness gain. After a single-label data transformation, the method propagates labels from supervised to unsupervised samples, as in the original approach, by assuming that samples from the same class are more closely connected through sequences of nearby samples than samples from distinct classes. Given that the procedure is more reliable in high-density regions of the feature space, an additional step repropagates labels from the maxima of a probability density function to correct possible labeling errors from the previous step. Finally, the data transformation is reversed to obtain multiple labels per sample. The new approach is experimentally validated on several datasets in comparison with state-of-the-art methods. (C) 2018 Elsevier Inc. All rights reserved. (AU)

FAPESP's process: 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms
Grantee:João Paulo Papa
Support type: Regular Research Grants
FAPESP's process: 13/20387-7 - Hyperparameter optimization in deep learning arquitectures
Grantee:João Paulo Papa
Support type: Scholarships abroad - Research
FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
Grantee:Alexandre Xavier Falcão
Support type: Research Projects - Thematic Grants