Efficient model selection for probabilistic K nearest neighbour classification

Ji Won Yoon, Nial Friel

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


Probabilistic K-nearest neighbour (PKNN) classification has been introduced to improve the performance of the original K-nearest neighbour (KNN) classification algorithm by explicitly modelling uncertainty in the classification of each feature vector. However, an issue common to both KNN and PKNN is to select the optimal number of neighbours, K. The contribution of this paper is to incorporate the uncertainty in K into the decision making, and consequently to provide improved classification with Bayesian model averaging. Indeed the problem of assessing the uncertainty in K can be viewed as one of statistical model selection which is one of the most important technical issues in the statistics and machine learning domain. In this paper, we develop a new functional approximation algorithm to reconstruct the density of the model (order) without relying on time consuming Monte Carlo simulations. In addition, the algorithms avoid cross validation by adopting Bayesian framework. The performance of the proposed approaches is evaluated on several real experimental datasets.

Original languageEnglish
Pages (from-to)1098-1108
Number of pages11
Issue numberPB
Publication statusPublished - 2015 Feb 3

Bibliographical note

Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning ( NRF-2013R1A1A1012797 ). Nial Friels research was supported by Science Foundation Ireland under Grant no. 07/CE/I1147 and 12/IP/1424 .

Publisher Copyright:
© 2014 Elsevier B.V.


  • Bayesian inference
  • K-free model order estimation
  • Model averaging

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


Dive into the research topics of 'Efficient model selection for probabilistic K nearest neighbour classification'. Together they form a unique fingerprint.

Cite this