• DocumentCode
    1136769
  • Title

    Myopic Policies in Sequential Classification

  • Author

    Ben-Bassat, Moshe

  • Author_Institution
    Center for the Critically Ill, University of Southern California School of Medicine
  • Issue
    2
  • fYear
    1978
  • Firstpage
    170
  • Lastpage
    174
  • Abstract
    Several rules for feature selection in myopic policy are examined for solving the sequential finite classification problem with conditionally independent binary features. The main finding is that no rule is consistently superior to the others. Likewise no specific strategy for the alternating of rules seems to be significantly more efficient.
  • Keywords
    Classification; divergence measures; feature selection; information measures; myopic policies; probability of misclassification; sequential decisions; simulation; Automata; Costs; Dynamic programming; Gold; Inference algorithms; Large-scale systems; Medical simulation; Sequential analysis; Testing; Turing machines; Classification; divergence measures; feature selection; information measures; myopic policies; probability of misclassification; sequential decisions; simulation;
  • fLanguage
    English
  • Journal_Title
    Computers, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9340
  • Type

    jour

  • DOI
    10.1109/TC.1978.1675054
  • Filename
    1675054
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1136769