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IJCAI
2001

Using Text Classifiers for Numerical Classification

13 years 5 months ago
Using Text Classifiers for Numerical Classification
Consider a supervised learning problem in which examples contain both numerical- and text-valued features. To use traditional featurevector-based learning methods, one could treat the presence or absence of a word as a Boolean feature and use these binary-valued features together with the numerical features. However, the use of a text-classification system on this is a bit more problematic -- in the most straight-forward approach each number would be considered a distinct token and treated as a word. This paper presents an alternative approach for the use of text classification methods for supervised learning problems with numerical-valued features in which the numerical features are converted into bag-of-words features, thereby making them directly usable by text classification methods. We show that even on purely numerical-valued data the results of textclassification on the derived text-like representation outperforms the more naive numbers-as-tokensrepresentation and,more importan...
Sofus A. Macskassy, Haym Hirsh, Arunava Banerjee,
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2001
Where IJCAI
Authors Sofus A. Macskassy, Haym Hirsh, Arunava Banerjee, Aynur A. Dayanik
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