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http://dx.doi.org/10.3745/KIPSTB.2009.16B.6.479

Construction Scheme of Training Data using Automated Exploring of Boundary Categories  

Choi, Yun-Jeong (서일대학 정보통신과)
Jee, Jeong-Gyu (한국연구재단 연구기반조성단장)
Park, Seung-Soo (이화여자대학교 컴퓨터공학과)
Publication Information
The KIPS Transactions:PartB / v.16B, no.6, 2009 , pp. 479-488 More about this Journal
Abstract
This paper shows a reinforced construction scheme of training data for improvement of text classification by automatic search of boundary category. The documents laid on boundary area are usually misclassified as they are including multiple topics and features. which is the main factor that we focus on. In this paper, we propose an automated exploring methodology of optimal boundary category based on previous research. We consider the boundary area among target categories to new category to be required training, which are then added to the target category sementically. In experiments, we applied our method to complex documents by intentionally making errors in training process. The experimental results show that our system has high accuracy and reliability in noisy environment.
Keywords
Machine Learning; Learning/Training Algorithms; Active Learning; Hierarchical Classification; Clustering;
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