Refereed full papers (journals, book chapters, international conferences)

2010

@inproceedings{ZSS2010,
	vgclass =	{refpap},
	author =	{Zaidi, Nayyar Abbas and David McG.\ Squire and David
	Suter},
	title =	{{BoostML}: {A}n Adaptive Metric Learning for Nearest Neighbor
	Classification},
	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 =	{http://dx.doi.org/10.1007/978-3-642-13657-3_17},
	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.},
}