Abstract：A kernel-based nominal data classification (KNDC) method is proposed with a new distance definition and a simple inner product computing method in this paper. It’s insensitivity to outliers and classification capability to unbalanced data in real datasets are further analyzed. The calculation on inner product of nominal data is difficult, often regarded as the bottleneck of SVM. The KNDC possesses a lower computation complexity than SVM over the nominal dataset, which is discussed for its validity. Experimental results on the standard datasets demonstrate that the proposed method has promising performance compared with other methods. Key wordskernel-based classification method; nominal dataset; dissimilarity measure; inner production calculation
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2010, Vol. 24 
