• DocumentCode
    116239
  • Title

    Formal analysis of cross-validation for rule induction using probabilistic indices

  • Author

    Tsumoto, Shusaku ; Hirano, Shoji

  • Author_Institution
    Dept. of Med. Inf., Shimane Univ., Izumo, Japan
  • fYear
    2014
  • fDate
    18-20 Aug. 2014
  • Firstpage
    416
  • Lastpage
    423
  • Abstract
    This paper proposes a new framework for evaluation of cross-validation methods for rule induction based on statistical indices by using incremental sampling scheme. Although incremental sampling scheme is used for incremental rule induction, this paper shows that the same idea can be used for deletion of exampling, called decremental sampling scheme. Then, we applied this technique to the cross-validation method for rules define by the propositions whose constraints were define by inequalities of accuracy and coverage. The results show that the evaluation framework gives a powerful tool for evaluation of cross-validation.
  • Keywords
    learning (artificial intelligence); rough set theory; sampling methods; cross-validation evaluation; cross-validation methods; decremental sampling scheme; formal analysis; incremental rule induction; incremental sampling scheme; probabilistic indices; Accuracy; Educational institutions; Error analysis; Estimation; Probabilistic logic; Set theory; Training; accuracy; coverage; cross-validation; incremental sampling scheme; rule induction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cognitive Informatics & Cognitive Computing (ICCI*CC), 2014 IEEE 13th International Conference on
  • Conference_Location
    London
  • Print_ISBN
    978-1-4799-6080-4
  • Type

    conf

  • DOI
    10.1109/ICCI-CC.2014.6921492
  • Filename
    6921492