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| Adaptive Pseudo Nearest Neighbor Classification Based on BP Neural Network |
| ZENG Yong SHU Huan HU Jiangping GE Yueyue |
| (School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China) |
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Abstract Distance-weighted coefficients between unlabeled sample point and its nearest neighbors belonging to same sample set are determined subjectively in the Pseudo Nearest Neighbor (PNN) classification algorithm, which makes it difficult to obtain optimal distance-weighted value. In this paper, an adaptive pseudo neighbor classification algorithm based on BP neural network is proposed. Firstly, the distance-weighted values between unlabeled sample point and its neighbors lying in the same sample set are regarded as the input of BP neural network. Secondly, the corresponding distance-weighted values are adaptively determined according to the mapping between the inputs and outputs of BP neural network. Finally, the classification of unlabeled sample point is judged by the outputs of BP neural network. Experimental results show that the proposed approach adaptively adjusts the distance-weighted coefficients. Moreover, the classification accuracy can be effectively improved.
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Received: 29 January 2016
Published: 08 September 2016
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| Fund: The National Natural Science Foundation of China (61104104, 61473061), The Fund of Sichuan Provincial Key Laboratory of Signal and Information Processing (SZJJ2009-002) |
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Corresponding Authors:
SHU Huan
E-mail: shuhuan163@163.com
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