Feature Selection Based on Ant Colony Optimization and Rough Set Theory

被引：5
|
作者：
He, Ming [1 ]
机构：
[1] Beijing Univ Technol, Coll Comp Sci, Beijing, Peoples R China
来源：
ISCSCT 2008: INTERNATIONAL SYMPOSIUM ON COMPUTER SCIENCE AND COMPUTATIONAL TECHNOLOGY, VOL 1, PROCEEDINGS | 2008年
关键词：
rough set; ant colony optimization; feature selection; core;
D O I：
10.1109/ISCSCT.2008.43
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Ant colony optimization (ACO) algorithms have been applied successfully to combinatorial optimization problems. Rough set theory offers a viable approach for feature selection from data sets. In this paper, the basic concepts of rough set theory and ant colony optimization are introduced, and the role of the basic constructs of rough set approach in feature selection, namely attribute reduction is studied. Base above research, a rough set and ACO based algorithm for feature selection problems is proposed. Finally, the presented algorithm was tested on UCI data sets and performed effectively.
引用
收藏
页码：247 / 250
页数：4
相关论文
共 50 条
  • [41] Ant Colony Optimization Based Feature Selection Method for QEEG Data Classification
    Erguzel, Turker Tekin
    Ozekes, Serhat
    Gultekin, Selahattin
    Tarhan, Nevzat
    PSYCHIATRY INVESTIGATION, 2014, 11 (03) : 243 - 250
  • [42] A wrapper-filter feature selection technique based on ant colony optimization
    Manosij Ghosh
    Ritam Guha
    Ram Sarkar
    Ajith Abraham
    Neural Computing and Applications, 2020, 32 : 7839 - 7857
  • [43] An improved feature selection algorithm based on graph clustering and ant colony optimization
    Ghimatgar, Hojat
    Kazemi, Kamran
    Helfroush, Mohamamd Sadegh
    Aarabi, Ardalan
    KNOWLEDGE-BASED SYSTEMS, 2018, 159 : 270 - 285
  • [44] Feature subset selection based on ant colony optimization and support vector machine
    Wang, Wan-liang
    Jiang, Yong
    Chen, S. Y.
    PROCEEDINGS OF THE 7TH WSEAS INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, COMPUTATIONAL GEOMETRY AND ARTIFICIAL VISION (ISCGAV'-07), 2007, : 184 - +
  • [45] SemiACO: A semi-supervised feature selection based on ant colony optimization
    Karimi, Fereshteh
    Dowlatshahi, Mohammad Bagher
    Hashemi, Amin
    EXPERT SYSTEMS WITH APPLICATIONS, 2023, 214
  • [46] A wrapper-filter feature selection technique based on ant colony optimization
    Ghosh, Manosij
    Guha, Ritam
    Sarkar, Ram
    Abraham, Ajith
    NEURAL COMPUTING & APPLICATIONS, 2020, 32 (12): : 7839 - 7857
  • [47] Pattern Matching based Classification using Ant Colony Optimization based Feature Selection
    Sreeja, N. K.
    Sankar, A.
    APPLIED SOFT COMPUTING, 2015, 31 : 91 - 102
  • [48] Feature selection algorithms using Rough Set Theory
    Caballero, Yail
    Alvarez, Delia
    Bel, Rafael
    Garcia, Maria M.
    PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, 2007, : 407 - 411
  • [49] Mammography feature selection using rough set theory
    Pethalakshmi, A.
    Thangave, K.
    Jaganathan, P.
    2006 INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING AND COMMUNICATIONS, VOLS 1 AND 2, 2007, : 237 - +
  • [50] An Efficient Feature Selection Using Ant Colony Optimization Algorithm
    Kabir, Md. Monirul
    Shahjahan, Md.
    Murase, Kazuyuki
    NEURAL INFORMATION PROCESSING, PT 2, PROCEEDINGS, 2009, 5864 : 242 - +
12345