Semi-supervised learning is crucial in many applications where accessing class labels is unaffordable or costly. The most promising approaches are graph-based but they are transductive and they do not provide a generalized model working on inductive scenarios. To address this problem, we propose a generic framework for inductive semi-supervised learning based on three components: an ensemble of semi-supervised autoencoders providing a new data representation that leverages the knowledge supplied by the reduced amount of available labels; a graph-based step that helps augmenting the training set with pseudo-labeled instances and, finally, a classifier trained with labeled and pseudo-labeled instances. The experimental results show that our framework outperforms state-of-the-art inductive semi-supervised methods.

An Inductive Framework for Semi-supervised Learning (Discussion Paper)

Shuyi Yang
First
;
Dino Ienco;Roberto Esposito;Ruggero G. Pensa
Co-last
2021-01-01

Abstract

Semi-supervised learning is crucial in many applications where accessing class labels is unaffordable or costly. The most promising approaches are graph-based but they are transductive and they do not provide a generalized model working on inductive scenarios. To address this problem, we propose a generic framework for inductive semi-supervised learning based on three components: an ensemble of semi-supervised autoencoders providing a new data representation that leverages the knowledge supplied by the reduced amount of available labels; a graph-based step that helps augmenting the training set with pseudo-labeled instances and, finally, a classifier trained with labeled and pseudo-labeled instances. The experimental results show that our framework outperforms state-of-the-art inductive semi-supervised methods.
2021
29th Italian Symposium on Advanced Database Systems (SEBD 2021)
Pizzo Calabro (VV), Italy
September 5-9, 2021
Proceedings of the 29th Italian Symposium on Advanced Database Systems (SEBD 2021)
CEUR-WS.org
2994
371
378
http://ceur-ws.org/Vol-2994/paper41.pdf
semi-supervised learning, graph-based algorithms, inductive methods
Shuyi Yang, Dino Ienco, Roberto Esposito, Ruggero G. Pensa
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1815411
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