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

The K Nearest Neighbors (KNN) classifier is widely used in many fields suchas fingerprint-based localization or medicine. It determines the classmembership of unlabelled sample based on the class memberships of the Klabelled samples, the so-called nearest neighbors, that are closest to theunlabelled sample. The choice of K has been the topic of various studies andproposed KNN-variants. Yet no variant has been proven to outperform all othervariants. In this paper a KNN-variant is discussed which ensures that the Knearest neighbors are indeed close to the unlabelled sample and finds K alongthe way. The algorithm is tested and compared to the standard KNN intheoretical scenarios and for indoor localization based on ion-mobilityspectrometry fingerprints. It achieves a higher classification accuracy thanthe KNN in the tests, while having the same computational demand.

Flexible K Nearest Neighbors Classifier: Derivation and Application for Ion-mobility Spectrometry-based Indoor Localization