模式识别与人工智能
Thursday, Apr. 3, 2025 Home      About Journal      Editorial Board      Instructions      Ethics Statement      Contact Us                   中文
  2020, Vol. 33 Issue (9): 820-829    DOI: 10.16451/j.cnki.issn1003-6059.202009006
Papers and Reports Current Issue| Next Issue| Archive| Adv Search |
Online Streaming Feature Selection for High-Dimensional and Class-Imbalanced Data Based on Max-Decision Boundary
LIN Yaojin1,2, CHEN Xiangyan1,2, BAI Shengxing1,2, WANG Chenxi1,2
1. School of Computer Science and Engineering, Minnan Normal University, Zhangzhou 363000
2. Key Laboratory of Data Science and Intelligence Application, The Education Department of Fujian Province, Minnan Normal University, Zhangzhou 363000

Download: PDF (732 KB)   HTML (1 KB) 
Export: BibTeX | EndNote (RIS)      
Abstract  The feature space of data changes with time dynamically. The number of features on training data is high-dimensional and fixed, and the label space is imbalanced. Motivated by the above, an online streaming feature selection algorithm for high-dimensional and class-imbalanced data based on max-decision boundary is proposed. An adaptive neighborhood relation is defined with consideration of the effect of boundary samples based on neighborhood rough set, and then a rough dependency calculation formula with respect to max-decision boundary is designed. Meanwhile, three online feature subset evaluation metrics are proposed to select features with great discriminability in majority and minority classes. Experiments on eleven high-dimensional and class-imbalanced datasets indicate that the proposed method achieves better performance than some state-of-the-art online streaming feature selection algorithms.
Key wordsOnline Feature Selection      High-Dimensional and Class-Imbalanced Data      Adaptive Neighborhood      Neighborhood Rough Set     
Received: 01 July 2020     
ZTFLH: TP 18  
Fund:National Natural Science Foundation of China(No.61672272), Natural Science Foundation of Fujian Province(No.2018J01548,2018J01547), Science and Technology Project of the Education Department of Fujian Province(No.JAT180318)
Corresponding Authors: LIN Yaojin, Ph.D., professor. His research interests include data mining and machine learning.   
About author:: CHEN Xiangyan, master student. His research interests include data mining.BAI Shengxing, master student. His research interests include data mining.WANG Chenxi, master, lecturer. Her research interests include data mining.
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
LIN Yaojin
CHEN Xiangyan
BAI Shengxing
WANG Chenxi
Cite this article:   
LIN Yaojin,CHEN Xiangyan,BAI Shengxing等. Online Streaming Feature Selection for High-Dimensional and Class-Imbalanced Data Based on Max-Decision Boundary[J]. , 2020, 33(9): 820-829.
URL:  
http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202009006      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I9/820
Copyright © 2010 Editorial Office of Pattern Recognition and Artificial Intelligence
Address: No.350 Shushanhu Road, Hefei, Anhui Province, P.R. China Tel: 0551-65591176 Fax:0551-65591176 Email: bjb@iim.ac.cn
Supported by Beijing Magtech  Email:support@magtech.com.cn