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Prototype based Classification by Generating Multidimensional Spheres per Class Area
Journal of The Korea Society of Computer and Information
Abbr :
JKSCI
2015, 20(2), pp.21-28
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science
2015, 20(2), pp.21-28
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science
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In this paper, we propose a prototype-based classification learning by using the nearest-neighbor rule. The nearest-neighbor is applied to segment the class area of all the training data into spheres withinwhich the data exist from the same class. Prototypes are the center of spheres and their radii arecomputed by the mid-point of the two distances to the farthest same class point and the nearest anotherclass point. And we transform the prototype selection problem into a set covering problem in order to determine the smallest set of prototypes that include all the training data. The proposed prototypeselection method is based on a greedy algorithm that is applicable to the training data per class. Thecomplexity of the proposed method is not complicated and the possibility of its parallel implementation ishigh. The prototype-based classification learning takes up the set of prototypes and predicts the class oftest data by the nearest neighbor rule. In experiments, the generalization performance of our prototypeclassifier is superior to those of the nearest neighbor, Bayes classifier, and another prototype classifier.
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