Semi-Supervised Ordinal Classification Framework Based on Information Fusion

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.