Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/62322
Title: Hybrid k-nearest neighbor classifier
Authors: Yu, ZW
Chen, HT
Liu, JM
You, J 
Leung, HKN 
Han, GQ
Keywords: Classification
Ensemble learning
Machine learning
Nearest neighbor classifier
Supervised learning
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on cybernetics, 2016, v. 46, no. 6, p. 1263-1275 How to cite?
Journal: IEEE transactions on cybernetics 
Abstract: Conventional k-nearest neighbor (KNN) classification approaches have several limitations when dealing with some problems caused by the special datasets, such as the sparse problem, the imbalance problem, and the noise problem. In this paper, we first perform a brief survey on the recent progress of the KNN classification approaches. Then, the hybrid KNN (HBKNN) classification approach, which takes into account the local and global information of the query sample, is designed to address the problems raised from the special datasets. In the following, the random subspace ensemble framework based on HBKNN (RS-HBKNN) classifier is proposed to perform classification on the datasets with noisy attributes in the high-dimensional space. Finally, the nonparametric tests are proposed to be adopted to compare the proposed method with other classification approaches over multiple datasets. The experiments on the real-world datasets from the Knowledge Extraction based on Evolutionary Learning dataset repository demonstrate that RS-HBKNN works well on real datasets, and outperforms most of the state-of-the-art classification approaches.
URI: http://hdl.handle.net/10397/62322
ISSN: 2168-2267
EISSN: 2168-2275
DOI: 10.1109/TCYB.2015.2443857
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