Simultaneous Feature and Model Selection for High-Dimensional Data

被引：0
|
作者：
Perolini, Alessandro [1 ]
Guerif, Sebastien [2 ,3 ]
机构：
[1] Politecn Milan, Dipart Ingn Gestionale, Piazza L da Vinci 32, I-20133 Milan, Italy
[2] CNRS, Villetaneuse, France
[3] Univ Paris 13, F-75231 Paris 05, France
来源：
2011 23RD IEEE INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2011) | 2011年
关键词：
Feature selection; model selection; Support Vector Machines; classification performance; CLASSIFICATION; CANCER;
D O I：
10.1109/ICTAI.2011.16
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The paper proposes an Evolutionary-based method to improve the prediction performance of Support Vector Machines classifiers applied to both artificial and real-world datasets which suffer from the curse of dimensionality. This method performs a simultaneous feature and model selection to discover the subset of features and the SVM parameters' values which provide a low prediction error. Moreover, it does not require a pre-processing step to filter the features so it can be applied to a whole dataset.
引用
收藏
页码：47 / 50
页数：4
相关论文
共 50 条
  • [1] Simultaneous Feature Selection and Classification for High-Dimensional Data
    Pai, Vriddhi
    Gupta, Subhash Chand
    PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON GREEN COMPUTING AND INTERNET OF THINGS (ICGCIOT 2018), 2018, : 153 - 158
  • [2] A filter feature selection for high-dimensional data
    Janane, Fatima Zahra
    Ouaderhman, Tayeb
    Chamlal, Hasna
    JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY, 2023, 17
  • [3] Feature selection for high-dimensional temporal data
    Tsagris, Michail
    Lagani, Vincenzo
    Tsamardinos, Ioannis
    BMC BIOINFORMATICS, 2018, 19
  • [4] Feature selection for high-dimensional data
    Bolón-Canedo V.
    Sánchez-Maroño N.
    Alonso-Betanzos A.
    Progress in Artificial Intelligence, 2016, 5 (2) : 65 - 75
  • [5] Feature selection for high-dimensional data
    Destrero A.
    Mosci S.
    De Mol C.
    Verri A.
    Odone F.
    Computational Management Science, 2009, 6 (1) : 25 - 40
  • [6] A novel minorization-maximization framework for simultaneous feature selection and clustering of high-dimensional count data
    Zamzami, Nuha
    Bouguila, Nizar
    PATTERN ANALYSIS AND APPLICATIONS, 2023, 26 (01) : 91 - 106
  • [7] Feature Selection for High-Dimensional Data Through Instance Vote Combining
    Chamakura, Lily
    Saha, Goutam
    PROCEEDINGS OF THE 7TH ACM IKDD CODS AND 25TH COMAD (CODS-COMAD 2020), 2020, : 161 - 169
  • [8] On the scalability of feature selection methods on high-dimensional data
    Bolon-Canedo, V.
    Rego-Fernandez, D.
    Peteiro-Barral, D.
    Alonso-Betanzos, A.
    Guijarro-Berdinas, B.
    Sanchez-Marono, N.
    KNOWLEDGE AND INFORMATION SYSTEMS, 2018, 56 (02) : 395 - 442
  • [9] A novel feature selection scheme for high-dimensional data sets: four-Staged Feature Selection
    Pehlivanli, Ayca Cakmak
    JOURNAL OF APPLIED STATISTICS, 2016, 43 (06) : 1140 - 1154
  • [10] Feature selection for high-dimensional imbalanced data
    Yin, Liuzhi
    Ge, Yong
    Xiao, Keli
    Wang, Xuehua
    Quan, Xiaojun
    NEUROCOMPUTING, 2013, 105 : 3 - 11
12345