• DocumentCode
    3382786
  • Title

    Functional gene prediction with vital reduced features: Further topics for feature reduction and evaluation criteria for classifiers

  • Author

    Xiaochuan Ai ; Jingbo Xia

  • Author_Institution
    Coll. of Sci., Naval Univ. of Eng., Wuhan, China
  • fYear
    2011
  • fDate
    15-16 Aug. 2011
  • Firstpage
    501
  • Lastpage
    506
  • Abstract
    Aiming at the prediction of protein solubility, four feature reduction methods are discussed in this paper, including Correlation coefficient method, Filter method, Relief method and Genetic method. With the Top 100 features discovered by genetic method, the best classifier achieves the accuracy of 86% and MCC of 0.7236 in Jackknife test. Moreover, further discussions about feature reduction and classifier reliability evaluation criteria are given. The author claim the exclusive importance of capacity of expansion prediction for classifiers.
  • Keywords
    biology computing; data mining; pattern classification; support vector machines; Jackknife test; SVM; correlation coefficient method; data mining; evaluation classifiers; evaluation criteria; feature reduction methods; filter method; functional gene prediction; genetic method; protein solubility; relief method; support vector machine; vital reduced features; Accuracy; Correlation; Feature extraction; Genetics; Proteins; Reliability; Support vector machines; Support vector machine; classification; cross validation; reliability; verification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Automation and Logistics (ICAL), 2011 IEEE International Conference on
  • Conference_Location
    Chongqing
  • ISSN
    2161-8151
  • Print_ISBN
    978-1-4577-0301-0
  • Electronic_ISBN
    2161-8151
  • Type

    conf

  • DOI
    10.1109/ICAL.2011.6024771
  • Filename
    6024771