Encoding Ordinal Features into Binary Features for Text Classification

Abstract

Abstract. 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.

Classifying websites by industry sector

“…All of these structural features are framed as binary features of the form c ≤ ai [3], where c is the count and ai is a natural number. For instance, UrlLength ≤ 8 is a feature whose value is 1 if the length of the URL of the website home page is smaller or equal than 8, and 0 otherwise.…”