数字农科院2.0

A Co-Training Approach For Sequential Three-Way Decisions

文献类型： 外文期刊

作者： Dai, D; Li, HX; Jia, XY; Zhou, XZ; Huang, B; Liang, SN

作者机构：

关键词： Three-way decisions; Cost-sensitive; Boundary region; Co-training; Confidence

期刊名称： INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS

ISSN： 1868-8071

年卷期： 2020 年 11 卷 5 期

页码：

收录情况： JCR（2021版） ; EI（2021版）

摘要： 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.

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