@article{(Open Science Index):https://publications.waset.org/pdf/16439,
	  title     = {Designing Early Warning System: Prediction Accuracy of Currency Crisis by Using k-Nearest Neighbour Method},
	  author    = {Nor Azuana Ramli and  Mohd Tahir Ismail and  Hooy Chee Wooi},
	  country	= {},
	  institution	= {},
	  abstract     = {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
	    journal   = {International Journal of Mathematical and Computational Sciences},
	  volume    = {7},
	  number    = {7},
	  year      = {2013},
	  pages     = {1134 - 1139},
	  ee        = {https://publications.waset.org/pdf/16439},
	  url   	= {https://publications.waset.org/vol/79},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 79, 2013},