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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Encoding Ordinal Features into Binary Features for Text Classification
verfasst von : Andrea Esuli, Fabrizio Sebastiani
Erschienen in: Advances in Information Retrieval
Verlag: Springer Berlin Heidelberg
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We propose a method by means of which supervised learning algorithms that only accept binary input can be extended to use ordinal (i.e., integer-valued) input. This is much needed in text classification, since it becomes thus possible to endow these learning devices with term frequency information, rather than just information on the presence/absence of the term in the document. We test two different learners based on “boosting”, and show that the use of our method allows them to obtain effectiveness gains. We also show that one of these boosting methods, once endowed with the representations generated by our method, outperforms an SVM learner with tfidf-weighted input.