A co-training approach for sequential three-way decisions

Abstract

In recent years, three-way decisions have received much attention in uncertain decision and cost-sensitive learning communities. However, in many real applications, labeled samples are usually far from sufficient. In this case, it is a reasonable choice to defer the decision rather than make an immediate decision without sufficient supported information, thus it constructs a boundary region. In order to label more available samples, a traditional co-training method employs two classifiers on two complementary views to extend the existing training sets. However, the wrong predictions of new labels may lead to a high misclassification cost, especially when few labeled samples are available. To address this problem, a co-training method is incorporated into three-way decisions, which can label new samples with higher confidence. When we obtain sufficient labeled samples, the non-commitment decisions are directly decided to a positive or a negative region, which finally generates a two-way decisions result. Experiments on several face databases are conducted to validate the effectiveness of the proposed approach.