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ICPR
2006
IEEE

The Generalized Condensed Nearest Neighbor Rule as A Data Reduction Method

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The Generalized Condensed Nearest Neighbor Rule as A Data Reduction Method
In this paper, we propose a new data reduction algorithm that iteratively selects some samples and ignores others that can be absorbed, or represented, by those selected. This algorithm differs from the condensed nearest neighbor (CNN) rule in its employment of a strong absorption criterion, in contrast to the weak criterion employed by CNN; hence, it is called the generalized CNN (GCNN) algorithm. The new criterion allows GCNN to incorporate CNN as a special case, and can achieve consistency, or asymptotic Bayes-risk efficiency, under certain conditions. GCNN, moreover, can yield significantly better accuracy than other instance-based data reduction methods. We demonstrate the last claim through experiments on five datasets, some of which contain a very large number of samples.
Bo-Han Kuo, Chien-Hsing Chou, Fu Chang
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2006
Where ICPR
Authors Bo-Han Kuo, Chien-Hsing Chou, Fu Chang
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