Character Variable Numeralization Based on Dimension Expanding and its Application on Text Classification

摘要

The character variable discrete numeralization destroyed the disorder of character variables. As text classification problem contains more character variable, discrete numeralization approach affects the classification performance of classifiers. In this paper, we propose a character variable numeralization algorithm based on dimension expanding. Firstly, the algorithm computes the number of different values which the character variable takes. Then it replaces the original values with the natural bases in the m-dimensional Euclidean space. Though the algorithm causes a dimension expanding, it reserves the disorder of character variables because the natural bases are no difference in size, so this algorithm is a better character variable numerical processing algorithm. Experiments on text classification data sets show that though the proposed algorithm costs a little more running time, its classification performance is better.