COMBINED UNSUPERVISED AND SEMI-SUPERVISED LEARNING FOR DATA CLASSIFICATION

Nenhuma Miniatura disponível

Data

2016-01-01

Orientador

Coorientador

Pós-graduação

Curso de graduação

Título da Revista

ISSN da Revista

Título de Volume

Editor

Ieee

Tipo

Trabalho apresentado em evento

Direito de acesso

Acesso abertoAcesso Aberto

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

Idioma

Inglês

Como citar

2016 Ieee 26th International Workshop On Machine Learning For Signal Processing (mlsp). New York: Ieee, 6 p., 2016.

Itens relacionados