Filter Feature Selection Performance Comparison in High-dimensional Data

被引：0
|
作者：
Huertas, Carlos [1 ]
Juarez-Ramirez, Reyes [1 ]
机构：
[1] Autonomous Univ Baja Calif, Dept Comp Sci, Tijuana, BC, Mexico
来源：
2014 17TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) | 2014年
关键词：
feature selection; filter; algorithm; high dimensional data;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The key idea behind feature selection is to find a subset of features that produce similar or better results as the original set while being more compact. Algorithms in this topic can be grouped in filter, wrapper and hybrid, however for very high dimensional data it has been found that the filter approach is better due to being less computational expensive. In this paper we provide a study about how information explosion has caused an impact on solutions for feature selection. A theoretical analysis is reviewed followed by an empirical comparison of 10 of the most popular filter algorithms with datasets ranging from 2400 up to 100,000 features in order to observe algorithm performance, scalability and detect current open problems. Results suggest that some of the current most popular solutions may become obsolete in the future due to the increase in dataset complexity.
引用
收藏
页数：8
相关论文
共 50 条
  • [1] A filter feature selection for high-dimensional data
    Janane, Fatima Zahra
    Ouaderhman, Tayeb
    Chamlal, Hasna
    [J]. JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY, 2023, 17
  • [2] Benchmark for filter methods for feature selection in high-dimensional classification data
    Bommert, Andrea
    Sun, Xudong
    Bischl, Bernd
    Rahnenfuehrer, Joerg
    Lang, Michel
    [J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2020, 143
  • [3] Feature selection for high-dimensional data
    Bolón-Canedo V.
    Sánchez-Maroño N.
    Alonso-Betanzos A.
    [J]. Progress in Artificial Intelligence, 2016, 5 (2) : 65 - 75
  • [4] Feature selection for high-dimensional data
    Destrero A.
    Mosci S.
    De Mol C.
    Verri A.
    Odone F.
    [J]. Computational Management Science, 2009, 6 (1) : 25 - 40
  • [5] FEATURE SELECTION FOR HIGH-DIMENSIONAL DATA ANALYSIS
    Verleysen, Michel
    [J]. NCTA 2011: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON NEURAL COMPUTATION THEORY AND APPLICATIONS, 2011, : IS23 - IS25
  • [6] Feature selection for high-dimensional imbalanced data
    Yin, Liuzhi
    Ge, Yong
    Xiao, Keli
    Wang, Xuehua
    Quan, Xiaojun
    [J]. NEUROCOMPUTING, 2013, 105 : 3 - 11
  • [7] Feature selection for high-dimensional data in astronomy
    Zheng, Hongwen
    Zhang, Yanxia
    [J]. ADVANCES IN SPACE RESEARCH, 2008, 41 (12) : 1960 - 1964
  • [8] Feature Selection with High-Dimensional Imbalanced Data
    Van Hulse, Jason
    Khoshgoftaar, Taghi M.
    Napolitano, Amri
    Wald, Randall
    [J]. 2009 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2009), 2009, : 507 - 514
  • [9] Feature selection for high-dimensional temporal data
    Michail Tsagris
    Vincenzo Lagani
    Ioannis Tsamardinos
    [J]. BMC Bioinformatics, 19
  • [10] Feature selection for high-dimensional temporal data
    Tsagris, Michail
    Lagani, Vincenzo
    Tsamardinos, Ioannis
    [J]. BMC BIOINFORMATICS, 2018, 19
12345