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IJDLS
2010
108views more  IJDLS 2010»
13 years 2 months ago
Sampling the Web as Training Data for Text Classification
Data acquisition is a major concern in text classification. The excessive human efforts required by conventional methods to build up quality training collection might not always b...
Wei-Yen Day, Chun-Yi Chi, Ruey-Cheng Chen, Pu-Jen ...
IPM
2008
196views more  IPM 2008»
13 years 4 months ago
Author identification: Using text sampling to handle the class imbalance problem
Authorship analysis of electronic texts assists digital forensics and anti-terror investigation. Author identification can be seen as a single-label multi-class text categorizatio...
Efstathios Stamatatos
ICDM
2003
IEEE
210views Data Mining» more  ICDM 2003»
13 years 10 months ago
CBC: Clustering Based Text Classification Requiring Minimal Labeled Data
Semi-supervised learning methods construct classifiers using both labeled and unlabeled training data samples. While unlabeled data samples can help to improve the accuracy of trai...
Hua-Jun Zeng, Xuanhui Wang, Zheng Chen, Hongjun Lu...
MICAI
2007
Springer
13 years 11 months ago
Taking Advantage of the Web for Text Classification with Imbalanced Classes
A problem of supervised approaches for text classification is that they commonly require high-quality training data to construct an accurate classifier. Unfortunately, in many real...
Rafael Guzmán-Cabrera, Manuel Montes-y-G&oa...
ICDM
2008
IEEE
164views Data Mining» more  ICDM 2008»
13 years 11 months ago
Classifying High-Dimensional Text and Web Data Using Very Short Patterns
In this paper, we propose the "Democratic Classifier", a simple, democracy-inspired patternbased classification algorithm that uses very short patterns for classificatio...
Hassan H. Malik, John R. Kender