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

This paper proposes a new model based on Fuzzy k-Nearest Neighbors forclassification with monotonic constraints, Monotonic Fuzzy k-NN (MonFkNN).Real-life data-sets often do not comply with monotonic constraints due to classnoise. MonFkNN incorporates a new calculation of fuzzy memberships, whichincreases robustness against monotonic noise without the need for relabeling.Our proposal has been designed to be adaptable to the different needs of theproblem being tackled. In several experimental studies, we show significantimprovements in accuracy while matching the best degree of monotonicityobtained by comparable methods. We also show that MonFkNN empirically achievesimproved performance compared with Monotonic k-NN in the presence of largeamounts of class noise.

Fuzzy k-Nearest Neighbors with monotonicity constraints: Moving towards the robustness of monotonic noise