Converting numerical classification into text classification

doi:10.1016/S0004-3702(02)00359-4

Converting numerical classification into text classification

2003-01-01

13

OA

AI

DOI：10.1016/S0004-3702(02)00359-4

原文链接

查看原文

摘要

En 中文

Consider a supervised learning problem in which examples contain both numerical- and text-valued 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. (C) 2002 Elsevier Science B.V All rights reserved.

Keyword:

machine learning

text classification

information retrieval

Converting numerical classification into text classification

摘要

AI总结

期刊

机构

引用论文