TY - JOUR
T1 - Hybrid κ-Nearest Neighbor Classifier
AU - Yu, Zhiwen
AU - Chen, Hantao
AU - LIU, Jiming
AU - You, Jane
AU - Leung, Hareton
AU - Han, Guoqiang
N1 - Funding Information:
This work was supported in part by the grant from the National Natural Science Foundation of China under Project 61472145
PY - 2016/6
Y1 - 2016/6
N2 - Conventional κ-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.
AB - Conventional κ-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.
KW - Classification
KW - ensemble learning
KW - machine learning
KW - nearest neighbor classifier
KW - supervised learning
UR - http://www.scopus.com/inward/record.url?scp=84933564979&partnerID=8YFLogxK
U2 - 10.1109/TCYB.2015.2443857
DO - 10.1109/TCYB.2015.2443857
M3 - Journal article
AN - SCOPUS:84933564979
SN - 2168-2267
VL - 46
SP - 1263
EP - 1275
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 6
M1 - 7137658
ER -