Efficient model selection for probabilistic K nearest neighbour classification

Files in This Item:
File Description SizeFormat 
insight_publication.pdf256.36 kBAdobe PDFDownload
Title: Efficient model selection for probabilistic K nearest neighbour classification
Authors: Won Yoon, Ji
Friel, Nial
Permanent link: http://hdl.handle.net/10197/8315
Date: 3-Feb-2015
Abstract: 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.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: Elsevier
Journal: Neurocomputing
Volume: 149
Issue: Part B
Start page: 1098
End page: 1108
Copyright (published version): 2014 Elsevier
Keywords: Machine learningStatisticsBayesian inferenceModel averagingK-free model order estimation
DOI: 10.1016/j.neucom.2014.07.023
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection
Insight Research Collection

Show full item record

Citations 50

Last Week
Last month
checked on Oct 11, 2018

Download(s) 50

checked on May 25, 2018

Google ScholarTM



This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.