Active learning by the naive credal classifier

Antonucci, Alessandro and Corani, Giorgio and Bernaschina, Sandra (2012) Active learning by the naive credal classifier. In: Communications in Computer and Information Science, Advances in Computational Intelligence UNSPECIFIED, Granada (Spain).

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In standard classification a training set of supervised instances is given. In a more general setup, some supervised instances are available, while further ones should be chosen from an unsupervised set and then annotated. As the annotation step is costly, active learn- ing algorithms are used to select which instances to annotate to maximally increase the classification performance while annotating only a limited number of them. Several active learning algorithms are based on the naive Bayes classifier. We work instead with the naive credal classifier, namely an extension of naive Bayes to imprecise probability. We propose two novel methods for active learning based on the naive credal classifier. Empir- ical comparisons show performance comparable or slightly superior to that of approaches solely based on the naive Bayes.

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