期刊
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
卷 11, 期 5, 页码 1129-1139出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s13042-020-01086-7
关键词
Three-way decisions; Cost-sensitive; Boundary region; Co-training; Confidence
资金
- National Natural Science Foundation of China [71671086, 61876079, 71732003, 61773208]
- National Key Research and Development Program of China [2016YFD0702100, 2018YFB1402600]
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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