presenter : fen-rou, ciou authors : toh koon charlie neo, dan ventura 2012, prl

Intelligent Database Systems Lab

Presenter : Fen-Rou, Ciou

Authors : Toh Koon Charlie Neo, Dan Ventura

2012, PRL

A direct boosting algorithm for the k-nearest neighbor classifier via local warping

of the distance metric


OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments


Motivation

• The k-nearest neighbor pattern classifier is an

effective learning algorithm, it can result in large

model sizes.


Objectives

• The paper present a direct boosting algorithm for the

k-NN classifier that creates an ensemble of models

with locally modified distance weighting to increase

the accuracy and condense the model size.


Methodology - Framework

AdaBoost

v = {+, }

Dzxi

x


Methodology

Sensitivity data order - Randomize - Batch update


Methodology

Voting mechanism - simple voting - error-weigh voting


Methodology

Condensing model size - optimal weight - average the weight


Experiments


Experiments

Fig 8. Boosted k-NN with randomized data order.Fig 9. Boosted k-NN with batch update. Fig 10. Boosted k-NN with error-weighted voting.Fig 11. Boosted k-NN with optimal weights. Fig 12. Boosted k-NN with average weights.


Experiments


Conclusions• The Boosted k-NN can boost the generalization

accuracy of the k-nearest neighbor algorithm.

• The Boosted k-NN algorithm modifier the decision

surface, producing a better solution.


Comments• Advantages– The paper describes rich experiment.

• Applications– classification

presenter : fen-rou, ciou authors : toh koon charlie neo, dan ventura 2012, prl

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