Data-driven Feature Selection Methods for Text Classification: an Empirical Evaluation

Data-driven Feature Selection Methods for Text Classification: an Empirical Evaluation

Dimensionality reduction is a crucial task in text classification. The most adopted strategy is feature selection using filter methods. This approach presents a difficulty in determining the best size for the final feature vector. At Least One FeaTure (ALOFT), Maximum f Features per Document (MFD),...

Full description

Bibliographic Details
Main Authors: Rogerio C. P. Fragoso, Roberto H. W. Pinheiro, George Cavalcanti
Format: Article
Language:English
Published: Graz University of Technology 2019-04-01
Series:Journal of Universal Computer Science
Subjects:
text classification
feature selection
Online Access:https://lib.jucs.org/article/22602/download/pdf/

Internet

https://lib.jucs.org/article/22602/download/pdf/

Similar Items