Learning classification models with soft-label information

Learning classification models with soft-label information

OBJECTIVE Learning of classification models in medicine often relies on data labeled by a human expert. Since labeling of clinical data may be time-consuming, finding ways of alleviating the labeling costs is critical for our ability to automatically learn such models. In this paper we propose a new machine learning approach that is able to learn improved binary classification models more effic...

Efficient Learning of Classification Models from Soft-label Information by Binning and Ranking

Construction of classification models from data in practice often requires additional human effort to annotate (label) observed data instances. However, this annotation effort may often be too costly and only a limited number of data instances may be feasibly labeled. The challenge is to find methods that let us reduce the number of the labeled instances but at the same time preserve the qualit...

Efficient Learning with Soft Label Information and Multiple Annotators

Learning with Privileged Information for Multi-Label Classification

In this paper, we propose a novel approach for learning multi-label classifiers with the help of privileged information. Specifically, we use similarity constraints to capture the relationship between available information and privileged information, and use ranking constraints to capture the dependencies among multiple labels. By integrating similarity constraints and ranking constraints into ...

Active Learning with Multi-Label SVM Classification

Multi-label classification, where each instance is assigned to multiple categories, is a prevalent problem in data analysis. However, annotations of multi-label instances are typically more timeconsuming or expensive to obtain than annotations of single-label instances. Though active learning has been widely studied on reducing labeling effort for single-label problems, current research on mult...