Robust Gaussian kernel based approach for feature selection

被引：4
|
作者：
Hsiao, Chih-Ching [1 ]
Chuang, Chen-Chia [2 ]
Su, Shun-Feng [3 ]
机构：
[1] Department of Information Technology, Kao Yuan University, Taiwan
[2] Department of Electrical Engineering, National Ilan University, Taiwan
[3] Department of Electrical Engineering, National Taiwan University of Science and Technology, Taiwan
来源：
Advances in Intelligent Systems and Computing | 2014年 / 268卷
关键词：
Rough set theory - Statistics;
D O I：
10.1007/978-3-319-05500-8_4
中图分类号：
学科分类号：
摘要：
The outlier problem of feature selection is rarely discussed in the most previous works. Moreover, there are no work has been reported in literature on symbolic interval feature selection in the supervised framework. In this paper, we will incorporate similarity margin concept and Gaussian kernel fuzzy rough sets to deal with the Symbolic Data Selection problem and it is also an optimizing problem. The advantage of this approach is it can easily introduce loss function and with robustness. © Springer International Publishing Switzerland 2014.
引用
收藏
页码：25 / 33
相关论文
共 50 条
  • [31] Double Gaussian based feature normalization for robust speech recognition
    Liu, B
    Dai, LR
    Li, JY
    Wang, RH
    2004 INTERNATIONAL SYMPOSIUM ON CHINESE SPOKEN LANGUAGE PROCESSING, PROCEEDINGS, 2004, : 253 - 256
  • [32] A Kernel Partial least square based feature selection method
    Talukdar, Upasana
    Hazarika, Shyamanta M.
    Gan, John Q.
    PATTERN RECOGNITION, 2018, 83 : 91 - 106
  • [33] KERNEL ENTROPY-BASED UNSUPERVISED SPECTRAL FEATURE SELECTION
    Zhang, Zhihong
    Hancock, Edwin R.
    INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2012, 26 (05)
  • [34] Kernel and Feature Selection for Visible and Infrared based Obstacle Recognition
    Apatean, Anca
    Rogozan, Alexandrina
    Bensrhair, Abdelaziz
    PROCEEDINGS OF THE 11TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS, 2008, : 1130 - +
  • [35] An approach for kernel selection based on data distribution
    Wang, Wenjian
    Guo, Jinling
    Men, Changqian
    ROUGH SETS AND KNOWLEDGE TECHNOLOGY, 2008, 5009 : 596 - +
  • [36] Multiple Kernel Learning based Feature Selection for Process Monitoring
    Yang, Sheng-Lan
    Weng, Wei
    Rong, Gang
    Feng, Yi-Ping
    2017 16TH IEEE/ACIS INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE (ICIS 2017), 2017, : 809 - 814
  • [37] An Ensemble Based Approach for Feature Selection
    Minaei-Bidgoli, Behrouz
    Asadi, Maryam
    Parvin, Hamid
    ENGINEERING APPLICATIONS OF NEURAL NETWORKS, PT I, 2011, 363 : 240 - 246
  • [38] Feature selection based on robust fuzzy rough sets using kernel-based similarity and relative classification uncertainty measures
    Liang, Pei
    Lei, Dingfei
    Chin, KwaiSang
    Hu, Junhua
    Knowledge-Based Systems, 2022, 255
  • [39] Feature selection based on robust fuzzy rough sets using kernel-based similarity and relative classification uncertainty measures
    Liang, Pei
    Lei, Dingfei
    Chin, KwaiSang
    Hu, Junhua
    KNOWLEDGE-BASED SYSTEMS, 2022, 255
  • [40] A Robust Approach for Multivariate Binary Vectors Clustering and Feature Selection
    Al Mashrgy, Mohamed
    Bouguila, Nizar
    Daoudi, Khalid
    NEURAL INFORMATION PROCESSING, PT II, 2011, 7063 : 125 - +
12345