Naive credal classifier 2: an extension of naive Bayes for delivering robust classifications

Naive credal classifier 2 (NCC2) extends naive Bayes in order to deliver more robust classifications. NCC2 is based on a set of prior densities rather than on a single prior; as a consequence, when faced with instances whose classification is prior-dependent (and therefore might not be reliable), it returns a set of classes (we call this an indeterminate classification) instead of a single class. Moreover, NCC2 introduces a very general and flexible treatment of missing data, which, under certain circumstances, can also lead to indeterminate classifications. In this case, indeterminacy can be regarded as a way to preserve reliability despite the information hidden by missing values. We call hard-to-classify the instances classified indeterminately by NCC2. Extensive empirical evaluations show that naive Bayes' accuracy drops considerably on the hard-toclassify instances identified by NCC2, and that on the other hand, NCC2 has high set-accuracy (the proportion of times that the act...

• Type: Conference
• Year: 2008
• Where: DMIN
• Authors: Giorgio Corani, Marco Zaffalon
• Keywords: Data Mining, DMIN 2008, Indeterminate Classifications, Naive Bayes, Naive Credal Classifier