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  • @inproceedings{ZSS2010,
    	vgclass =	{refpap},
    	author =	{Zaidi, Nayyar Abbas and David McG.\ Squire and David
    	title =	{{BoostML}: {A}n Adaptive Metric Learning for Nearest Neighbor
    	booktitle =	{Proceedings of the 14th Pacific-Asia Conference on
    	Knowledge Discovery and Data Mining},
    	address =	{Hyderabad, India},
    	number =	{6118},
    	series =	{Lecture Notes in Computer Science},
    	pages =	{142--149},
    	publisher =	{Springer-Verlag},
    	month =	{June~21--24},
    	year =	{2010},
    	doi =	{},
    	abstract =	{A Nearest Neighbor (NN) classifier assumes class
    	conditional probabilities to be locally smooth. This assumption is
    	often invalid in high dimensions and significant bias can be introduced
    	when using the nearest neighbor rule. This effect can be mitigated to
    	some extent by using a locally adaptive metric. In this work we propose
    	an adaptive metric learning algorithm that learns an optimal metric at
    	the query point. We learn a distance metric using a feature relevance
    	measure inspired by boosting. The modified metric results in a smooth
    	neighborhood that leads to better classification results. We tested our
    	technique on major UCI machine learning databases and compared the
    	results to state of the art techniques. Our method resulted in
    	significant improvements in the performance of the K-NN classifier and
    	also performed better than other techniques on major databases.},