Accuracy improvement of automatic text classification based on feature transformation

In this paper, we describe a comparative study on techniques of feature transformation and classification to improve the accuracy of automatic text classification. The normalization to the relative word frequency, the principal component analysis (K-L transformation) and the power transformation were applied to the feature vectors, which were classified by the Euclidean distance, the linear discriminant function, the projection distance, the modified projection distance and the SVM. Categories and Subject Descriptors I.5.4 [Pattern Recognition]: Applications –Text processing. General Terms Algorithms, Experimentation, Performance. Keywords automatic text classification, principal component analysis, variable transformation.

• Type: Conference
• Year: 2003
• Where: DOCENG
• Authors: Guowei Zu, Wataru Ohyama, Tetsushi Wakabayashi, Fumitaka Kimura
• Keywords: Automatic Text Classification, DOCENG 2003, Principal Component Analysis, Projection Distance