With the purpose of improving the accuracy of text categorization and reducing the dimension of
the feature space, this paper proposes a two-stage feature selection method based on a novel category correlation
degree (CCD) method and latent semantic indexing (LSI). In the first stage, a novel CCD method is proposed to
select the most effective features for text classification, which is more effective than the traditional feature selection
method. In the second stage, document representation requires a high dimensionality of the feature space and
does not take into account the semantic relation between features, which leads to a poor categorization accuracy.
So LSI method is proposed to solve these problems by using statistically derived conceptual indices to replace the
individual terms which can discover the important correlative relationship between features and reduce the feature
space dimension. Firstly, each feature in our algorithm is ranked depending on their importance of classification
using CCD method. Secondly, we construct a new semantic space based on LSI method among features. The
experimental results have proved that our method can reduce effectively the dimension of text vector and improve
the performance of text categorization.
WANG Fei (王飞), LI Cai-hong* (李彩虹), WANG Jing-shan (王景山),XU Jiao (徐娇), LI Lian (李廉)
. A Two-Stage Feature Selection Method for Text Categorization by Using Category Correlation Degree and Latent Semantic Indexing[J]. Journal of Shanghai Jiaotong University(Science), 2015
, 20(1)
: 44
-50
.
DOI: 10.1007/s12204-015-1586-y
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