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

Probabilistic k-nearest neighbour (PKNN) classification has been introducedto improve the performance of original k-nearest neighbour (KNN) classificationalgorithm by explicitly modelling uncertainty in the classification of eachfeature vector. However, an issue common to both KNN and PKNN is to select theoptimal number of neighbours, $k$. The contribution of this paper is toincorporate the uncertainty in $k$ into the decision making, and in so doinguse Bayesian model averaging to provide improved classification. Indeed theproblem of assessing the uncertainty in $k$ can be viewed as one of statisticalmodel selection which is one of the most important technical issues in thestatistics and machine learning domain. In this paper, a new functionalapproximation algorithm is proposed to reconstruct the density of the model(order) without relying on time consuming Monte Carlo simulations. In addition,this algorithm avoids cross validation by adopting Bayesian framework. Theperformance of this algorithm yielded very good performance on several realexperimental datasets.

Efficient Estimation of the number of neighbours in Probabilistic K Nearest Neighbour Classification