• DocumentCode
    130360
  • Title

    Parsimonious Naive Bayes

  • Author

    Boulle, Marc

  • Author_Institution
    Orange Labs., Lannion, France
  • fYear
    2014
  • fDate
    7-10 Sept. 2014
  • Firstpage
    355
  • Lastpage
    359
  • Abstract
    We describe our submission to the AAIA´14 Data Mining Competition, where the objective was to reach good predictive performance on text mining classification problems while using a small number of variables. Our submission was ranked 6th, less than 1% behind the winner. We also present an empirical study on the trade-off between parsimony of the representation and accuracy, and show how good performance can be obtained quickly and efficiently.
  • Keywords
    Bayes methods; data mining; pattern classification; parsimonious naive Bayes; text mining classification problems; trade-off; Accuracy; Bayes methods; Data mining; Input variables; Lead; Niobium; Optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Information Systems (FedCSIS), 2014 Federated Conference on
  • Conference_Location
    Warsaw
  • Type

    conf

  • DOI
    10.15439/2014F496
  • Filename
    6933037
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=130360