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Semi-supervised learning via complex networks: network construction, selection and propagation of labels and applications

Abstract

Graph-based semi-supervised learning (SSL) is a promising paradigm for modeling the manifold in multidimensional data space, and is effective in propagating a small number of initial labels to a large amount of unlabelled data. This approach has been used in a variety of applications, such as image segmentation and annotation, human speech recognition, text classification, etc. Recently, some authors observed the importance of the generated network for the label propagation process, but other aspects were still little investigated, for example, the selection of the initial labeled samples or the topological characteristics of the network. Thus, the objective of this project is to investigate in depth all the steps involved in SSL, including the selection of initial labels, the selection of the network to be built and the method for propagating labels, and propose new approaches to improve this process. Challenges of the area will also be addressed such as contaminated or unbalanced labels, large databases and applications in text mining, climate data analysis and data augmentation for images. (AU)

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VEICULO: TITULO (DATA)
VEICULO: TITULO (DATA)

Scientific publications (9)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
SANO, HUMBERTO HAYASHI; BERTON, LILIAN. Temporal Event Graph Approach and Robustness Analysis for Air Transport Networ. IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, v. 8, n. 4, p. 3453-3464, OCT 1 2021. Web of Science Citations: 0.
DUARTE, JOSE MARCIO; SOUSA, SAMUEL; MILIOS, EVANGELOS; BERTON, LILIAN. Deep analysis of word sense disambiguation via semi-supervised learning and neural word representations. INFORMATION SCIENCES, v. 570, p. 278-297, SEP 2021. Web of Science Citations: 0.
FREITAS, VANDER L. S.; YANCHUK, SERHIY; ZAKS, MICHAEL; MACAU, ELBERT E. N. Synchronization-based symmetric circular formations of mobile agents and the generation of chaotic trajectories. COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, v. 94, MAR 2021. Web of Science Citations: 2.
VEGA-OLIVEROS, DIDIER A.; ZHAO, LIANG; ROCHA, ANDERSON; BERTON, LILIAN. Link Prediction Based on Stochastic Information Diffusion. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, FEB 2021. Web of Science Citations: 1.
DE AQUINO AFONSO, BRUNO KLAUS; BERTON, LILIAN. Identifying noisy labels with a transductive semi-supervised leave-one-out filter. PATTERN RECOGNITION LETTERS, v. 140, p. 127-134, DEC 2020. Web of Science Citations: 0.
FERREIRA, LEONARDO N.; VEGA-OLIVEROS, DIDIER A.; ZHAO, LIANG; CARDOSO, MANOEL F.; MACAU, ELBERT E. N. Global fire season severity analysis and forecasting. Computers & Geosciences, v. 134, JAN 2020. Web of Science Citations: 1.
DE CASTRO SANTOS, MATHEUS A.; VEGA-OLIVEROS, DIDIER A.; ZHAO, LIANG; BERTON, LILIAN. Classifying El Ni & x00F1;o-Southern Oscillation Combining Network Science and Machine Learning. IEEE ACCESS, v. 8, p. 55711-55723, 2020. Web of Science Citations: 0.
VEGA-OLIVERO, DIDIER A.; GOMES, PEDRO SPOLJARIC; MILIOS, EVANGELOS E.; BERTON, LILIAN. A multi-centrality index for graph-based keyword extraction. INFORMATION PROCESSING & MANAGEMENT, v. 56, n. 6 NOV 2019. Web of Science Citations: 0.
VEGA-OLIVEROS, DIDIER A.; ZHAO, LIANG; BERTON, LILIAN. Evaluating link prediction by diffusion processes in dynamic networks. SCIENTIFIC REPORTS, v. 9, JUL 25 2019. Web of Science Citations: 0.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.