Abstract
In this paper, we propose a new reliable classification approach, called the pseudo nearest centroid neighbor rule, which is based on the pseudo nearest neighbor rule (PNN) and nearest centroid neighborhood (NCN). In the proposed PNCN, the nearest centroid neighbors rather than nearest neighbors per class are first searched by means of NCN. Then, we calculate k categorical local mean vectors corresponding to k nearest centroid neighbors, and assign the weight to each local mean vector. Using the weighted k local mean vectors for each class, PNCN designs the corresponding pseudo nearest centroid neighbor and decides the class label of the query pattern according to the closest pseudo nearest centroid neighbor among all classes. The classification performance of the proposed PNCN is evaluated on real data sets in terms of the classification accuracy. The experimental results demonstrate the effectiveness of PNCN over the competing methods in many practical classification problems.
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Acknowledgment
This work was supported by National Science Foundation of China (Grant Nos. 61162005, 41171338 and 61163002), the Beifang Ethnic University school project (Grant No. 2010Y030), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 14KJB520007), China Postdoctoral Science Foundation (Grant No. 2015M570411) and Research Foundation for Talented Scholars of JiangSu University (Grant No. 14JDG037).
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Ma, H., Wang, X., Gou, J. (2016). Pseudo Nearest Centroid Neighbor Classification. In: Hung, J., Yen, N., Li, KC. (eds) Frontier Computing. Lecture Notes in Electrical Engineering, vol 375. Springer, Singapore. https://doi.org/10.1007/978-981-10-0539-8_12
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DOI: https://doi.org/10.1007/978-981-10-0539-8_12
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