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 supervised learning, simple baseline classifiers can be constructed byonly looking at the class, i.e., ignoring any other information from thedataset. The single-label learning community frequently uses as a reference theone which always predicts the majority class. Although a classifier mightperform worse than this simple baseline classifier, this behaviour requires aspecial explanation. Aiming to motivate the community to compare experimentalresults with the ones provided by a multi-label baseline classifier, callingthe attention about the need of special explanations related to classifierswhich perform worse than the baseline, in this work we propose the use ofGeneral_B, a multi-label baseline classifier. General_B was evaluated incontrast to results published in the literature which were carefully selectedusing a systematic review process. It was found that a considerable number ofpublished results on 10 frequently used datasets are worse than or equal to theones obtained by General_B, and for one dataset it reaches up to 43% of thedataset published results. Moreover, although a simple baseline classifier wasnot considered in these publications, it was observed that even for very poorresults no special explanations were provided in most of them. We hope that thefindings of this work would encourage the multi-label community to consider theidea of using a simple baseline classifier, such that further explanations areprovided when a classifiers performs worse than a baseline.

Comparing published multi-label classifier performance measures to the ones obtained by a simple multi-label baseline classifier