Semi-Supervised Ensemble Learning for Dealing with Inaccurate and Incomplete Supervision

Abstract

In real-world tasks, obtaining a large set of noise-free data can be prohibitively expensive. Therefore, recent research tries to enable machine learning to work with weakly supervised datasets, such as inaccurate or incomplete data. However, the previous literature treats each type of weak supervision individually, although, in most cases, different types of weak supervision tend to occur simultaneously. Therefore, in this article, we present Smart MEnDR, a Classification Model that applies Ensemble Learning and Data-driven Rectification to deal with inaccurate and incomplete supervised datasets. The model first applies a preliminary phase of ensemble learning in which the noisy data points are detected while exploiting the unlabelled data. The phase employs a semi-supervised technique with maximum likelihood estimation to decide on the disagreement rate. Second, the proposed approach applies an iterative meta-learning step to tackle the problem of knowing which points should be made correct to improve the performance of the final classifier. To evaluate the proposed framework, we report the classification performance, noise detection, and the labelling accuracy of the proposed method against state-of-the-art techniques. The experimental results demonstrate the effectiveness of the proposed framework in detecting noise, providing correct labels, and attaining high classification performance.