Learning classification models with soft-label information.

ABSTRACT:

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 efficiently by refining the binary class information in the training phase with soft labels that reflect how strongly the human expert feels about the original class labels.

Materials and methods

Two types of methods that can learn improved binary classification models from soft labels are proposed. The first relies on probabilistic/numeric labels, the other on ordinal categorical labels. W

SUBMITTER: Nguyen Q 

PROVIDER: S-EPMC3994863 | biostudies-literature | 2014 May-Jun

REPOSITORIES: biostudies-literature