Updating Knowledge in Feedback-Based Multi-classifier Systems
Donato Impedovo, Giuseppe Pirlo · 2011
In pattern recognition tasks it is frequent that new (labeled) data became available as the specific application scenario evolves. When a multi expert system (ME) is adopted, the collective behavior of classifiers can be used to select the most profitable samples in order to update the knowledge base. More specifically a misclassified sample, for a particular classifier, is used to update that classifier only if that sample produces a misclassification by the ensemble of classifiers. This approach is compared to situation in which the entire new dataset is used for learning as well as the case in which specific samples are selected by the individual classifier. Successful results have been obtained by considering the CEDAR (handwritten digit) database, moreover it is also shown how they depend by the specific combination decision schema, as well as by data distribution.