학술논문
A cost-sensitive semi-supervised learning model based on uncertainty.
Document Type
Article
Author
Source
Subject
*SUPERVISED learning
*COST control
*SET theory
*UNCERTAINTY
*PREDICTION theory
*
*
*
*
Language
ISSN
0925-2312
Abstract
Aiming at reducing the total cost in cost-sensitive learning, this paper introduces a semi-supervised learning model based on uncertainty of sample outputs. Its central idea is (1) to categorize the samples which are not in training set into several groups based on the uncertainty-magnitude of their outputs, (2) to add the group of samples which have the least uncertainty together with their predicted labels in the original training set, and (3) to retain a new classifier for total cost reduction. The ratio of costs between classes and its impact on learning system improvement is discussed. Theoretical analysis and experimental demonstration show that the model can effectively improve the performance of a cost-sensitive learning algorithm for a certain type of classifiers. [ABSTRACT FROM AUTHOR]