Active Learning for Imbalanced Ordinal Regression

• Published in: IEEE access Vol. 8; pp. 180608 - 180617
• Main Authors: Ge, Jiaming, Chen, Haiyan, Zhang, Dongfang, Hou, Xiaye, Yuan, Ligang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Active learning; Algorithms; class imbalanced; Classification; Error analysis; evaluation method; Extraterrestrial measurements; Heuristic algorithms; Machine learning; Machine learning algorithms; Ordinal regression; Prediction algorithms; Regression analysis; State-of-the-art reviews; Statistical analysis; Statistical methods; Support vector machines; Time complexity; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3027764

Ordinal regression (OR), also called ordinal classification, is a special multi-classification designed for problems with ordered classes. Imbalanced data hinders the performance of classification algorithms, especially for OR algorithms, as imbalanced class distributions often arise in OR problems. In this article, we address an active learning based solution for imbalanced OR problem. We propose an active learning algorithm for OR (AL-OR) to select the most informative samples from unlabeled samples, mark them and add them to the training set. Based on AL-OR, we put forward an improved active learning for imbalanced OR (IAL-IOR), which further adjust the sampling strategy of AL-OR dynamically to make the training data as valuable and balanced as possible. Recall rate for multi-classification and new mean absolute error are designed to measure the performance of the algorithms. To the best of our knowledge, our algorithm is the first algorithm for imbalanced OR in algorithm level. The experimental results show that the proposed algorithms have faster convergence and much better generalization ability than the classical methods and the state-of-the-art methods under the evaluation measurements for imbalance problems. In addition, we also proved the effectiveness of our algorithms by statistical analysis.