English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this article we propose a novel ranking algorithm, referred to as HierLPR,for the multi-label classification problem when the candidate labels follow aknown hierarchical structure. HierLPR is motivated by a new metric called eAUCthat we design to assess the ranking of classification decisions. This metric,associated with the hit curve and local precision rate, emphasizes the accuracyof the first calls. We show that HierLPR optimizes eAUC under the treeconstraint and some light assumptions on the dependency between the nodes inthe hierarchy. We also provide a strategy to make calls for each node based onthe ordering produced by HierLPR, with the intent of controlling FDR ormaximizing F-score. The performance of our proposed methods is demonstrated onsynthetic datasets as well as a real example of disease diagnosis using NCBIGEO datasets. In these cases, HierLPR shows a favorable result over competingmethods in the early part of the precision-recall curve.

HierLPR: Decision making in hierarchical multi-label classification with local precision rates