%0 Journal Article
	%A Nor Azuana Ramli and  Mohd Tahir Ismail and  Hooy Chee Wooi
	%D 2013
	%J International Journal of Mathematical and Computational Sciences
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 79, 2013
	%T Designing Early Warning System: Prediction Accuracy of Currency Crisis by Using k-Nearest Neighbour Method
	%U https://publications.waset.org/pdf/16439
	%V 79
	%X Developing a stable early warning system (EWS)
model that is capable to give an accurate prediction is a challenging
task. This paper introduces k-nearest neighbour (k-NN) method
which never been applied in predicting currency crisis before with the
aim of increasing the prediction accuracy. The proposed k-NN
performance depends on the choice of a distance that is used where in
our analysis; we take the Euclidean distance and the Manhattan as a
consideration. For the comparison, we employ three other methods
which are logistic regression analysis (logit), back-propagation neural
network (NN) and sequential minimal optimization (SMO). The
analysis using datasets from 8 countries and 13 macro-economic
indicators for each country shows that the proposed k-NN method
with k = 4 and Manhattan distance performs better than the other

	%P 1134 - 1139