Title

DCPE co-training for classification

Document Type

Article

Date of Original Version

6-1-2012

Abstract

Co-training is a well-known semi-supervised learning technique that applies two basic learners to train the data source, which uses the most confident unlabeled data to augment labeled data in the learning process. In the paper, we use the diversity of class probability estimation (DCPE) between two learners and propose the DCPE co-training approach. The key idea is to use DCPE to predict labels for the unlabeled data in the training process. The experimental studies with UCI data demonstrate that the DCPE co-training is robust and efficient in classification. The comparative studies with supervised learning methods and semi-supervised learning methods also demonstrate the effectiveness of the proposed approach. © 2012 Elsevier B.V.

Publication Title

Neurocomputing

Volume

86

Share

COinS