The goal of designing an optimal nearest-neighbor classifier is to maximize the classification accuracy while minimizing the sizes of both the reference and feature sets. A novel intelligent genetic algorithm (IGA), which is superior to conventional genetic algorithms (GAs) in solving large parameter optimization problems, is used to effectively achieve this goal. It is shown empirically that the IGA-designed classifier outperforms existing GA-based and non-GA-based classifiers in terms of classification accuracy and the total number of parameters of the reduced sets.
|Number of pages||6|
|State||Published - 1 Jan 2002|
|Event||2002 Congress on Evolutionary Computation, CEC 2002 - Honolulu, HI, United States|
Duration: 12 May 2002 → 17 May 2002
|Conference||2002 Congress on Evolutionary Computation, CEC 2002|
|Period||12/05/02 → 17/05/02|