Abstract
The k-nearest neighbor classification rule (k-NNR) is a very simple, yet powerful nonparametric classification method. As a variant of the k-NNR, a nonparametric classification method based on the local mean vector has achieved good classification performance. In this paper, a new variant of the k-NNR, a nonparametric classification method based on the local mean vector and the class mean vector has been proposed. Not only the information of the local mean of the k nearest neighbors of the unlabeled pattern in each individual class but also the knowledge of the ensemble mean of each individual class are taken into account in this new classification method. The proposed classification method is compared with the k-NNR, and the local mean-based nonparametric classification in terms of the classification error rate on the unknown patterns. Experimental results confirm the validity of this new classification approach.
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© 2008 Springer-Verlag Berlin Heidelberg
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Yong, Z., Bing, W., Liang, Z., Yang, YP. (2008). Nonparametric Classification Based on Local Mean and Class Mean. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2008. Lecture Notes in Computer Science, vol 5226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87442-3_74
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DOI: https://doi.org/10.1007/978-3-540-87442-3_74
Publisher Name: Springer, Berlin, Heidelberg
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