Fuzzy feature selection

被引：35
|
作者：
Rezaee, MR [1 ]
Goedhart, B [1 ]
Lelieveldt, BPF [1 ]
Reiber, JHC [1 ]
机构：
[1] Leiden Univ, Med Ctr, Dept Radiol, Div Image Proc, NL-2300 RC Leiden, Netherlands
来源：
PATTERN RECOGNITION | 1999年 / 32卷 / 12期
基金：
俄罗斯科学基金会;
关键词：
feature selection; fuzzy sets; multi-dimensional data analysis; fuzzy neural network; pattern recognition;
D O I：
10.1016/S0031-3203(99)00005-9
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In fuzzy classifier systems the classification is obtained by a number of fuzzy If-Then rules including linguistic terms such as Low and High that fuzzify each feature. This paper presents a method by which a reduced linguistic (fuzzy) set of a labeled multi-dimensional data set can be identified automatically. After the projection of the original data set onto a fuzzy space, the optimal subset of fuzzy features is determined using conventional search techniques. The applicability of this method has been demonstrated by reducing the number of features used for the classification of four real-world data sets. This method can also be used to generate an initial rule set for a fuzzy neural network. (C) 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
引用
收藏
页码：2011 / 2019
页数：9
相关论文
共 50 条
  • [21] Feature Subset Selection Using a Fuzzy Method
    Cintra, Marcos Evandro
    Martin, Trevor P.
    Monard, Maria Carolina
    Camargo, Heloisa de Arruda
    [J]. 2009 INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS, VOL 2, PROCEEDINGS, 2009, : 214 - +
  • [22] A fuzzy clustering based algorithm for feature selection
    Sun, HJ
    Wang, SR
    Mei, Z
    [J]. 2002 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-4, PROCEEDINGS, 2002, : 1993 - 1998
  • [23] Feature selection in a fuzzy student sectioning algorithm
    Amintoosi, M
    Haddadnia, J
    [J]. PRACTICE AND THEORY OF AUTOMATED TIMETABLING V, 2005, 3616 : 147 - 160
  • [24] An Efficient Fuzzy Rough Approach for Feature Selection
    Xu, Feifei
    Pan, Weiguo
    Wei, Lai
    Du, Haizhou
    [J]. ROUGH SETS AND KNOWLEDGE TECHNOLOGY, 2011, 6954 : 95 - +
  • [25] Feature Selection via Maximizing Fuzzy Dependency
    Hu, Qinghua
    Zhu, Pengfei
    Liu, Jinfu
    Yang, Yongbin
    Yu, Daren
    [J]. FUNDAMENTA INFORMATICAE, 2010, 98 (2-3) : 167 - 181
  • [26] Fuzzy Set Similarity for Feature Selection in Classification
    Cross, Valerie
    Zmuda, Michael
    Paul, Rahul
    Hall, Lawrence
    [J]. 2020 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), 2020,
  • [27] A new feature selection criterion for fuzzy classification
    Almeida, R. J.
    Silva, C. A.
    Sousa, J. M. C.
    [J]. 2006 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-5, 2006, : 437 - +
  • [28] Feature Selection Using Fuzzy Objective Functions
    Vieira, Susana M.
    Sousa, Joao M. C.
    Kaymak, Uzay
    [J]. PROCEEDINGS OF THE JOINT 2009 INTERNATIONAL FUZZY SYSTEMS ASSOCIATION WORLD CONGRESS AND 2009 EUROPEAN SOCIETY OF FUZZY LOGIC AND TECHNOLOGY CONFERENCE, 2009, : 1673 - 1678
  • [29] Feature Subset Selection for Fuzzy Classification Methods
    Cintra, Marcos E.
    Camargo, Heloisa A.
    [J]. INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS: THEORY AND METHODS, PT 1, 2010, 80 : 318 - +
  • [30] PROTOTYPE CLASSIFICATION AND FEATURE SELECTION WITH FUZZY SETS
    BEZDEK, JC
    CASTELAZ, PF
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1977, 7 (02): : 87 - 92
12345