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| Label Noise Filtering via Perception of Nearest Neighbors |
| JIANG Gaoxia1, FAN Ruixuan1, WANG Wenjian1,2 |
1. School of Computer and Information Technology, Shanxi University, Taiyuan 030006
2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006 |
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Abstract Label noise filtering algorithms based on k nearest neighbor are sensitive to the neighbor parameter k. Aiming at this problem, a label noise filtering algorithm based on perception of nearest neighbors(PNN) is proposed to solve the problem of intra-class label noise in binary classification datasets effectively. Positive and negative samples are considered separately in PNN, and thus the label noise detection problem in classification is transformed into two outlier detection problems with single-class data. Firstly, the personalized neighbor parameter is determined automatically by the neighbor perception strategy to avoid the sensitivity of neighbor parameter. Secondly, all samples are divided into core samples and non-core samples by noise factor. The non-core samples are taken as the candidates of label noise. Finally, the noise is identified and filtered by combining the label information of the nearest neighbors of the candidate samples. Experiments indicate that the proposed algorithm performs well in noise filtering and classification prediction.
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Received: 12 June 2020
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| Fund:National Natural Science Foundation of China(No.61673249,U1805263,61906113), Key R&D Program of Shanxi Province International Cooperation(No.201903D421050), Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi(No.2020L0007) |
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Corresponding Authors:
WANG Wenjian, Ph.D., professor. Her research interests include machine learning and intelligent computing.
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| About author:: JIANG Gaoxia, Ph.D., lecturer. His research interests include machine learning and data mining. FAN Ruixuan, master student. Her research interests include machine learning. |
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2020, Vol. 33 
