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http://dx.doi.org/10.3745/KIPSTB.2005.12B.7.811

Reinforcement Method for Automated Text Classification using Post-processing and Training with Definition Criteria  

Choi, Yun-Jeong (이화여자대학교 컴퓨터학과)
Park, Seung-Soo (이화여자대학교 컴퓨터학과)
Publication Information
The KIPS Transactions:PartB / v.12B, no.7, 2005 , pp. 811-822 More about this Journal
Abstract
Automated text categorization is to classify free text documents into predefined categories automatically and whose main goals is to reduce considerable manual process required to the task. The researches to improving the text categorization performance(efficiency) in recent years, focused on enhancing existing classification models and algorithms itself, but, whose range had been limited by feature based statistical methodology. In this paper, we propose RTPost system of different style from i.ny traditional method, which takes fault tolerant system approach and data mining strategy. The 2 important parts of RTPost system are reinforcement training and post-processing part. First, the main point of training method deals with the problem of defining category to be classified before selecting training sample documents. And post-processing method deals with the problem of assigning category, not performance of classification algorithms. In experiments, we applied our system to documents getting low classification accuracy which were laid on a decision boundary nearby. Through the experiments, we shows that our system has high accuracy and stability in actual conditions. It wholly did not depend on some variables which are important influence to classification power such as number of training documents, selection problem and performance of classification algorithms. In addition, we can expect self learning effect which decrease the training cost and increase the training power with employing active learning advantage.
Keywords
Automated Text Categorization(classification); Active Learning.; Self Learning; Hierarchical Classification; Text Mining; Data Mining; Fault Detection;
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Times Cited By KSCI : 1  (Citation Analysis)
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