D-Confidence: An active learning strategy to reduce label disclosure complexity in the presence of imbalanced class distributions

• Type: Article in International Scientific Journal
• Date: 2012

Authors

Escudeiro, NF (Author) Other; Jorge, AM (Author) FCUP

Journal

Title: Journal of the Brazilian Computer Society

Vol. 18

Pages: 311-330

ISSN: 0104-6500

Publisher: Springer Nature

Indexing

Scopus - 4 Citations

Other information

Authenticus ID: P-008-6M3

DOI: 10.1007/s13173-012-0069-3

Abstract (EN): In some classification tasks, such as those related to the automatic building and maintenance of text corpora, it is expensive to obtain labeled instances to train a classifier. In such circumstances it is common to have massive corpora where a few instances are labeled (typically a minority) while others are not. Semi-supervised learning techniques try to leverage the intrinsic information in unlabeled instances to improve classification models. However, these techniques assume that the labeled instances cover all the classes to learn which might not be the case. Moreover, when in the presence of an imbalanced class distribution, getting labeled instances from minority classes might be very costly, requiring extensive labeling, if queries are randomly selected. Active learning allows asking an oracle to label new instances, which are selected by criteria, aiming to reduce the labeling effort. D-Confidence is an active learning approach that is effective when in presence of imbalanced training sets. In this paper we evaluate the performance of d-Confidence in comparison to its baseline criteria over tabular and text datasets. We provide empirical evidence that d-Confidence reduces label disclosure complexity-which we have defined as the number of queries required to identify instances from all classes to learn-when in the presence of imbalanced data. © 2012 The Brazilian Computer Society.

Language: English

Type (Professor's evaluation): Scientific