COMBINED UNSUPERVISED AND SEMI-SUPERVISED LEARNING FOR DATA CLASSIFICATION
Nenhuma Miniatura disponível
Data
2016-01-01
Autores
Breve, Fabricio Aparecido [UNESP]
Guimaraes Pedronette, Daniel Carlos [UNESP]
IEEE
Título da Revista
ISSN da Revista
Título de Volume
Editor
Ieee
Resumo
Semi-supervised learning methods exploit both labeled and unlabeled data items in their training process, requiring only a small subset of labeled items. Although capable of drastically reducing the costs of labeling process, such methods are directly dependent on the effectiveness of distance measures used for building the kNN graph. On the other hand, unsupervised distance learning approaches aims at capturing and exploiting the dataset structure in order to compute a more effective distance measure, without the need of any labeled data. In this paper, we propose a combined approach which employs both unsupervised and semi-supervised learning paradigms. An unsupervised distance learning procedure is performed as a pre-processing step for improving the kNN graph effectiveness. Based on the more effective graph, a semi-supervised learning method is used for classification. The proposed Combined Unsupervised and Semi-Supervised Learning (CUSSL) approach is based on very recent methods. The Reciprocal kNN Distance is used for unsupervised distance learning tasks and the semi-supervised learning classification is performed by Particle Competition and Cooperation (PCC). Experimental results conducted in six public datasets demonstrated that the combined approach can achieve effective results, boosting the accuracy of classification tasks.
Descrição
Palavras-chave
Semi-Supervised Learning, Unsupervised Learning, Data Classification
Como citar
2016 Ieee 26th International Workshop On Machine Learning For Signal Processing (mlsp). New York: Ieee, 6 p., 2016.