Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2
Giorgio Corani, Marco Zaffalon; 9(20):581−621, 2008.
In this paper, the naive credal classifier, which is a set-valued counterpart of naive Bayes, is extended to a general and flexible treatment of incomplete data, yielding a new classifier called naive credal classifier 2 (NCC2). The new classifier delivers classifications that are reliable even in the presence of small sample sizes and missing values. Extensive empirical evaluations show that, by issuing set-valued classifications, NCC2 is able to isolate and properly deal with instances that are hard to classify (on which naive Bayes accuracy drops considerably), and to perform as well as naive Bayes on the other instances. The experiments point to a general problem: they show that with missing values, empirical evaluations may not reliably estimate the accuracy of a traditional classifier, such as naive Bayes. This phenomenon adds even more value to the robust approach to classification implemented by NCC2.
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