In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) will be discontinued after Dec. 31st, 2024.
See More
Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 4
1051-1057

Cancer classification method based on feature interaction and weight integration

Chen Haonan
Jin Min
College of Computer Science & Electronic Engineering, Hunan University, Changsha 410082, China
DOI: 10.19734/j.issn.1001-3695.2020.04.0106

Abstract

In the field of cancer classification, gene expression profile data has the characteristics of high dimensions, high redundancy, and unbalanced class distribution, which are the factors that affect the accuracy of classification. In order to improve the accuracy of cancer classification, this paper proposed a cancer classification method based on feature interaction and weight integration. At the feature selection level, this method used the gaining interaction of multiple features to select the features with the joint mutual information that was greater than the sum of the individual mutual information, and further used conditional mutual information to select low-redundancy features. At the classification model level, the re-learning ensemble model combined with weight integration feedback mechanism could comprehensively consider the different fitting ability of multiple models for different types of samples. This model constructed class weight that did not depend on the number of samples, and solved the problem of unbalanced class distribution. Comparative experiments of six kinds of cancer data show that the four indicators of accuracy, sensitivity, precision and F-measure are all stable above 99.39%, and the specificity is above 94.74%, which indicates that the method can further improve the accuracy and stability of cancer classification and the versatility of different cancers.

Foundation Support

国家自然科学基金资助项目(61773157)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.04.0106
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 4
Section: Algorithm Research & Explore
Pages: 1051-1057
Serial Number: 1001-3695(2021)04-016-1051-07

Publish History

[2021-04-05] Printed Article

Cite This Article

陈昊楠, 金敏. 基于特征交互与权重集成的癌症分类方法 [J]. 计算机应用研究, 2021, 38 (4): 1051-1057. (Chen Haonan, Jin Min. Cancer classification method based on feature interaction and weight integration [J]. Application Research of Computers, 2021, 38 (4): 1051-1057. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)