LAUSR

Abstract Uncertainty is a fundamental and unavoidable feature in daily life, which is the same for a single classifier. Thus, combining the predictions of many different classifiers is a very successful way to reduce the uncertainty. In this paper, we present a Correcting Reliability Level (CRL) supervised three-way decision (3WD) cascade model to implement image classification tasks. Our model simulates the human decision process by using 3WD to judge “certainty” or “uncertainty” of the classification result. When judged as “uncertainty”, CRL will supervise the 3WD and learn more information to make the final prediction. In addition, we introduce two Class Grouping methods to mining the relation between labels, which help us to train several expert ConvNets for different types of images. Experimental results show that our model can effectively reduce the classification error rate compared with the base classifier.