Decision Tree and Ensemble Learning Algorithms with Their Applications in Bioinformatics

被引:148
|
作者
Che, Dongsheng [1 ]
Liu, Qi [3 ]
Rasheed, Khaled [2 ]
Tao, Xiuping [4 ]
机构
[1] E Stroudsburg Univ, Dept Comp Sci, E Stroudsburg, PA 18301 USA
[2] Univ Georgia, Dept Comp Sci, Athens, GA 30602 USA
[3] Tongji Univ, Coll Life Sci & Biotechnol, Shanghai 200092, Peoples R China
[4] Winston Salem State Univ, Dept Chem, Winston Salem, NC 27110 USA
关键词
CANCER; CLASSIFICATION;
D O I
10.1007/978-1-4419-7046-6_19
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Machine learning approaches have wide applications in bioinformatics, and decision tree is one of the successful approaches applied in this field. In this chapter, we briefly review decision tree and related ensemble algorithms and show the successful applications of such approaches on solving biological problems. We hope that by learning the algorithms of decision trees and ensemble classifiers, biologists can get the basic ideas of how machine learning algorithms work. On the other hand, by being exposed to the applications of decision trees and ensemble algorithms in bioinformatics, computer scientists can get better ideas of which bioinformatics topics they may work on in their future research directions. We aim to provide a platform to bridge the gap between biologists and computer scientists.
引用
收藏
页码:191 / 199
页数:9
相关论文
共 50 条
  • [1] An evolving oblique decision tree ensemble architecture for continuous learning applications
    Christou, Ioannis T.
    Efremidis, Sofoklis
    [J]. ARTIFICIAL INTELLIGENCE AND INNOVATIONS 2007: FROM THEORY TO APPLICATIONS, 2007, : 3 - +
  • [2] Tree Edit Distance Problems: Algorithms and Applications to Bioinformatics
    Akutsu, Tatsuya
    [J]. IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2010, E93D (02): : 208 - 218
  • [3] Structural diversity for decision tree ensemble learning
    Tao Sun
    Zhi-Hua Zhou
    [J]. Frontiers of Computer Science, 2018, 12 : 560 - 570
  • [4] Structural diversity for decision tree ensemble learning
    Sun, Tao
    Zhou, Zhi-Hua
    [J]. FRONTIERS OF COMPUTER SCIENCE, 2018, 12 (03) : 560 - 570
  • [5] Classification of Parkinson's Disease by Decision Tree Based Instance Selection and Ensemble Learning Algorithms
    Li, Yongming
    Yang, Liuyang
    Wang, Pin
    Zhang, Cheng
    Xiao, Jie
    Zhang, Yanling
    Qiu, Mingguo
    [J]. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, 2017, 7 (02) : 444 - 452
  • [6] t-Tree and t-Forest: Decision Tree and Random Forest Algorithms Including the Relevance Factor with Applications in Bioinformatics
    Ashari, Zhila Esna
    Broschat, Shira L.
    [J]. 2019 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM), 2019, : 2779 - 2783
  • [7] Decision Tree Ensemble Hardware Accelerators for Embedded Applications
    Struharik, R.
    [J]. IEEE 13TH INTERNATIONAL SYMPOSIUM ON INTELLIGENT SYSTEMS AND INFORMATICS (SISY), 2015, : 101 - 106
  • [8] Ensemble Learning with Decision Tree for Remote Sensing Classification
    Pal, Mahesh
    [J]. PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, VOL 26, PARTS 1 AND 2, DECEMBER 2007, 2007, 26 : 735 - 737
  • [9] Performance analysis of advanced decision tree-based ensemble learning algorithms for landslide susceptibility mapping
    Sahin, Emrehan Kutlug
    Colkesen, Ismail
    [J]. GEOCARTO INTERNATIONAL, 2021, 36 (11) : 1253 - 1275
  • [10] Ensemble deep learning in bioinformatics
    Cao, Yue
    Geddes, Thomas Andrew
    Yang, Jean Yee Hwa
    Yang, Pengyi
    [J]. NATURE MACHINE INTELLIGENCE, 2020, 2 (09) : 500 - 508