Converting numerical classification into text classification

✍ Scribed by Sofus A. Macskassy; Haym Hirsh; Arunava Banerjee; Aynur A. Dayanik

• Publisher: Elsevier Science
• Year: 2003
• Tongue: English
• Weight: 243 KB
• Volume: 143
• Category: Article
• ISSN: 0004-3702
• DOI: 10.1016/s0004-3702(02)00359-4

✦ Synopsis

Consider a supervised learning problem in which examples contain both numerical-and textvalued features. To use traditional feature-vector-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 text classification on the derived text-like representation outperforms the more naive numbers-as-tokens representation and, more importantly, is competitive with mature numerical classification methods such as C4.5, Ripper, and SVM. We further show that on mixed-mode data adding numerical features using our approach can improve performance over not adding those features.