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Refinement method of post-processing and training for improvement of automated text classification

Title
Refinement method of post-processing and training for improvement of automated text classification
Authors
Choi, YJPark, SS
Ewha Authors
박승수
SCOPUS Author ID
박승수scopus
Issue Date
2006
Journal Title
COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2006, PT 2
ISSN
0302-9743JCR Link
Citation
vol. 3981, pp. 298 - 308
Publisher
SPRINGER-VERLAG BERLIN
Indexed
SCOPUS WOS scopus
Abstract
The paper presents a method for improving text classification by using examples that are difficult to classify. Generally, researches to improve the text categorization performance are focused on enhancing existing classification models and algorithms itself, but the range of which has been limited by the feature-based statistical methodology. In this paper, we propose a new method to improve the accuracy and the performance using refinement training and post-processing. Especially, we focused on complex documents that are generally considered to be hard to classify. Our proposed method has a different style from traditional classification methods, and take a data mining strategy and fault tolerant system approaches. In experiments, we applied our system to documents which usually get low classification accuracy because they are laid on a decision boundary. The result shows that our system has high accuracy and stability in actual conditions.
ISBN
3-540-34072-6
Appears in Collections:
엘텍공과대학 > 컴퓨터공학과 > Journal papers
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