TY - GEN
T1 - Accuracy improvement of automatic text classification based on feature transformation
AU - Zu, Guowei
AU - Ohyama, Wataru
AU - Wakabayashi, Tetsushi
AU - Kimura, Fumitaka
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2003
Y1 - 2003
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=3543055806&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=3543055806&partnerID=8YFLogxK
U2 - 10.1145/958238.958242
DO - 10.1145/958238.958242
M3 - Conference contribution
AN - SCOPUS:3543055806
SN - 1581137249
SN - 9781581137248
T3 - Proceedings of the 2003 ACM Symposium on Document Engineering
SP - 118
EP - 120
BT - Proceedings of the 2003 ACM Symposium on Document Engineering
PB - Association for Computing Machinery
T2 - Proceedings of the 2003 ACM Symposium on Document Engineering
Y2 - 20 November 2003 through 22 November 2003
ER -