关键词: 半监督顺序回归, 最近邻, 无标签样本, 折扣因子

Abstract: The supervised ordinal regression algorithm often requires large amount of labeled samples. However, in the real applications, labeling instances is time and labor consuming, and sometimes even unrealistic. Therefore, a semi-supervised ordinal regression algorithm was proposed, which learned from both the labeled and unlabeled examples. The proposed method began by choosing some instances from unlabeled dataset that are most similar to one labeled example in labeled dataset, and assigning them the corresponding ranker. At this stage, the nearest neighbor rule was packed to score the similarity of two instances. Then, by using supervised ordinal regression, the ranking model was trained from both the labeled and the newly labeled examples. The experimental results show this method produce statistically significant improvements with respect to ranking measures. On the other hand, discount factor λ was introduced for evaluating creditable degree of new labeled examples, and how λ and the size of labeled dataset affected the method performance was discussed.

Key words: semi-supervised ordinal regression, nearest neighbor, unlabeled sample, discount factor