Corani, Giorgio and Zaffalon, Marco (2008) Naive credal classifier 2: an extension of naive Bayes for delivering robust classifications. In: International Conference on Data Mining 2008 (DMIN '08), 14.07.2008-17.07.2008, USA.
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Abstract
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 actual class is contained in the set of returned classes) when it is indeterminate.
| Item Type: | Article in conference proceedings or Presentation at a conference (Paper) |
|---|---|
| Subjects: | Computer sciences > Artificial intelligence > Machine learning |
| Department/unit: | Dipartimento tecnologie innovative > Istituto Dalle Molle di studi sull’intelligenza artificiale USI-SUPSI |
| Depositing User: | Giorgio Corani |
| Date Deposited: | 12 May 2014 09:37 |
| Last Modified: | 06 May 2016 05:59 |
| URI: | http://repository.supsi.ch/id/eprint/4731 |
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