Batch-mode active ordinal classification based on expected model output change and leadership tree

While numerous batch-mode active learning (BMAL) methods have been developed for nominal classification, the absence of a BMAL method tailored for ordinal classification is conspicuous. This paper focuses on proposing an effective BMAL method for ordinal classification and argues that a BMAL method should guarantee that the selected instances in each iteration are highly informative, diverse from labeled instances, and diverse from each other. We first introduce an expected model output change criterion based on the kernel extreme learning machine-based ordinal classification model and demonstrate that the criterion is a composite containing both informativeness assessment and diversity assessment. Selecting instances with high scores of this criterion can ensure that the selected are highly informative and diverse from labeled instances. To ensure that the selected instances are diverse from each other, we propose a leadership tree-based batch instance selection approach, drawing inspiration from density peak clustering algorithm. Thus, our BMAL method can select a batch of peak-scoring points from different high-scoring regions in each iteration. The effectiveness of the proposed method is empirically examined through comparisons with several state-of-the-art BMAL methods.