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Myopic Policies in Sequential Classification
February 1978 (vol. 27 no. 2)
pp. 170-174
M. Ben-Bassat, Center for the Critically Ill, University of Southern California School of Medicine
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.
Index Terms:
simulation, Classification, divergence measures, feature selection, information measures, myopic policies, probability of misclassification, sequential decisions
Citation:
M. Ben-Bassat, "Myopic Policies in Sequential Classification," IEEE Transactions on Computers, vol. 27, no. 2, pp. 170-174, Feb. 1978, doi:10.1109/TC.1978.1675054
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