A Boosting Method Utilizing Reliably Labeled Data

Abstract

We address a novel and realistic Label Reliability Problem that belongs to the field of supervised learning, where con dence of labeling is different for each training set. Our main idea is to make more precise classi ers by dealing with reliably and not reliably labeled sets seperately. We focus on a novel boosting method that utilizes reliably labeled data. The theoretical investigation on the method makes clear its relation to soft margin approach, cost-sensitive learning and semisupervised learning. We perform detailed experiments that include the boosting method and 8 related methods. The results suggest the superiority of our approach that counts on unreliable labels.