New ensemble machine learning method for classification and prediction on gene expression data

被引：0
|
作者：
Wang, Ching Wei [1 ]
机构：
[1] Univ Lincoln, Vis & Artificial Intelligence Grp, Dept Comp & Informat, Lincoln LN6 7TS, England
来源：
2006 28TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-15 | 2006年
关键词：
ensemble machine learning; pattern recognition; microarray;
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
A reliable and precise classification of tumours is essential for successful treatment of cancer. Recent researches have confirmed the utility of ensemble machine learning algorithms for gene expression data analysis. In this paper, a new ensemble machine learning algorithm is proposed for classification and prediction on gene expression data. The algorithm is tested and compared with three popular adopted ensembles, i.e. bagging, boosting and arcing. The results show that the proposed algorithm greatly outperforms existing methods, achieving high accuracy over 12 gene expression datasets.
引用
收藏
页码：60 / 63
页数：4
相关论文
共 50 条
  • [1] Dissimilarity based ensemble of extreme learning machine for gene expression data classification
    Lu, Hui-juan
    An, Chun-lin
    Zheng, En-hui
    Lu, Yi
    [J]. NEUROCOMPUTING, 2014, 128 : 22 - 30
  • [2] Ensemble learning method for classification: Integrating data envelopment analysis with machine learning
    An, Qingxian
    Huang, Siwei
    Han, Yuxuan
    Zhu, You
    [J]. COMPUTERS & OPERATIONS RESEARCH, 2024, 169
  • [3] Ensemble based fuzzy weighted extreme learning machine for gene expression classification
    Wang, Yang
    Wang, Anna
    Ai, Qing
    Sun, Haijing
    [J]. APPLIED INTELLIGENCE, 2019, 49 (03) : 1161 - 1171
  • [4] Ensemble based fuzzy weighted extreme learning machine for gene expression classification
    Yang Wang
    Anna Wang
    Qing Ai
    Haijing Sun
    [J]. Applied Intelligence, 2019, 49 : 1161 - 1171
  • [5] A support vector machine ensemble for cancer classification using gene expression data
    Liao, Chen
    Li, Shutao
    [J]. BIOINFORMATICS RESEARCH AND APPLICATIONS, PROCEEDINGS, 2007, 4463 : 488 - +
  • [6] Ensemble dependence model for classification and prediction of cancer and normal gene expression data
    Qiu, P
    Wang, ZJ
    Liu, KJR
    [J]. BIOINFORMATICS, 2005, 21 (14) : 3114 - 3121
  • [7] An Ensemble Extreme Learning Machine for Data Stream Classification
    Yang, Rui
    Xu, Shuliang
    Feng, Lin
    [J]. ALGORITHMS, 2018, 11 (07)
  • [8] Cancer Classification of Gene Expression Data using Machine Learning Models
    De Guia, Joseph M.
    Devaraj, Madhavi
    Vea, Larry A.
    [J]. 2018 IEEE 10TH INTERNATIONAL CONFERENCE ON HUMANOID, NANOTECHNOLOGY, INFORMATION TECHNOLOGY, COMMUNICATION AND CONTROL, ENVIRONMENT AND MANAGEMENT (HNICEM), 2018,
  • [9] Gene expression data classification using topology and machine learning models
    Tamal K. Dey
    Sayan Mandal
    Soham Mukherjee
    [J]. BMC Bioinformatics, 22
  • [10] Gene expression data classification using topology and machine learning models
    Dey, Tamal K.
    Mandal, Sayan
    Mukherjee, Soham
    [J]. BMC BIOINFORMATICS, 2022, 22 (SUPPL 10)
12345