Corani, Giorgio and Zaffalon, Marco (2009) Lazy naive credal classifier. In: 1st ACM SIGKDD Workshop on Knowledge Discovery From Uncertain Data, 28.06.2009-01.07.2009, Paris, France.
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Abstract
We propose a local (or lazy) version of the naive credal classifier. The latter is an extension of naive Bayes to imprecise probability developed to issue reliable classifications despite small amounts of data, which may then be carrying highly uncertain information about a domain. Reliability is maintained because credal classifiers can issue set-valued classifications on instances that are particularly difficult to classify. We show by extensive experiments that the local classifier outperforms the original one, both in terms of accuracy of classification and because it leads to stronger conclusions (i.e., set-valued classifications made by fewer classes). By comparing the local credal classifier with a local version of naive Bayes, we also show that the former reliably deals with instances which are difficult to classify, unlike the local naive Bayes which leads to fragile classifications.
Item Type: | Article in conference proceedings or Presentation at a conference (Paper) |
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Subjects: | Mathematical sciences > Statistics > Statistical modelling 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 09:35 |
Last Modified: | 06 May 2016 05:54 |
URI: | http://repository.supsi.ch/id/eprint/4727 |
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