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.

中文翻译：

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顺序三路决策的联合训练方法

近年来，三向决策已在不确定的决策和对成本敏感的学习社区中引起了广泛关注。但是，在许多实际应用中，标记的样品通常远远不够。在这种情况下，推迟决策而不是在没有足够支持信息的情况下立即做出决策是一个合理的选择，因此，它会构建边界区域。为了标记更多可用样本，传统的协同训练方法在两个互补的视图上使用两个分类器来扩展现有的训练集。但是，对新标签的错误预测可能会导致较高的误分类成本，尤其是在没有可用标签样品的情况下。为了解决这个问题，将共训练方法结合到三向决策中，从而可以以更高的置信度标记新样本。当我们获得足够的带标记的样本时，将非承诺决策直接决策为正或负区域，最终生成双向决策结果。在几个面部数据库上进行了实验，以验证所提出方法的有效性。