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http://dx.doi.org/10.5351/KJAS.2006.19.1.183

Comparison Studies of Classification Methods based on L1-Distance and L1-Data Depth  

Baek Soo-Jin (Division of Bacterial Respiratory Infections, Center for Infectious Disease Research, Korea Center for Disease Control & Prevention, Korean National Institute of Health)
Hwang Jin-Soo (Department of Statistics, Inha University)
Kim Jean-Kyung (Department of Statistics, Inha University)
Publication Information
The Korean Journal of Applied Statistics / v.19, no.1, 2006 , pp. 183-193 More about this Journal
Abstract
We consider a new classification method(DnDclass) combining two classification rules based on $L_1$-distance(L1DISTclass) and $L_1$-data depth(L1DDclass). To investigate characteristics and to evaluate the performance of these classification methods, we use simulation data in various settings. Through this simulation study, we can confirm that the new method, DnDclass, performs relatively well in many cases.
Keywords
$L_1$-distance; $L_1$-data depth; classification;
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