Abstract:

In ordinal classification, class labels have a natural order. It has been widely studied in various fields, such as movie ratings and age estimation. Most existing methods assume that all samples are labeled. However, the unique nature of data often makes the collection of extensive labeled data challenging, thereby affecting the performance of ordinal classification. This study proposes a semi-supervised ordinal classification framework that incorporates additional information. The framework starts by generating partial order information from the relationships among unlabeled samples and constructing a directed graph network. Then, it uses Graph Neural Network (GNN) to aggregate neighbor information, enrich node representations, and capture the order between nodes, thereby recovering global rankings from partial order information. Subsequently, the method applies a Gaussian mixture model for feature weighting according to global rankings and employs clustering to assign pseudo labels by integrating this information into ordered information. Finally, the framework uses supervised learning models for ordinal classification tasks such as age estimation. Experiments on the FGNET, Adience, and UTKFace datasets show that the framework achieves reliable performance with fewer labeled data. It performs better than semi-supervised learning baselines in terms of Mean Absolute Error (MAE) and Accuracy. Specifically, MAE decreases by 0.05, 0.04, and 0.04, and Accuracy increases by 4.8, 4.5, and 3.5 percentage points on the three datasets, respectively.

Key words: ordinal classification, Graph Neural Network (GNN), feature weighting, information fusion, semi-supervised learning

摘要：

有序分类属于分类的一种，其要求类标签存在自然顺序，在很多领域例如电影分级、年龄估计都得到了广泛的研究。目前，大部分有序分类方法假设所有样本都被标记。但由于数据的特殊性，在实践中往往难以收集大量的标记数据，影响有序分类的性能。针对以上问题，提出一种结合额外信息的半监督有序分类框架。首先，利用未标记样本的顺序关系生成额外的偏序信息，并将偏序信息构建为有向图网络；然后使用图神经网络(GNN)聚合邻居信息，丰富节点表示，同时捕捉节点间的顺序关系，利用学习到的表示恢复偏序信息间的全局排名；接着使用高斯混合加权的方法对数据特征根据全局排名进行加权，并使用聚类方法为全局排名赋予伪标签，从而将这些信息合并到有序信息中；最后，使用有监督学习的有序分类模型进行年龄估计。在FGNET、Adience、UTKFace 3个数据集上的实验结果表明，该框架使用较少的标记数据便能够取得可靠的性能，在平均绝对误差(MAE)、准确率(Accuracy) 2个指标上相较于半监督学习基线方法均有提升：MAE在3个数据集上分别降低了0.05、0.04、0.04，Accuracy在3个数据集上分别提高了4.8、4.5、3.5百分点。

关键词: 有序分类, 图神经网络, 特征加权, 信息融合, 半监督学习