상세정보
학술지
Context-Based Classification for Harmful Web Documents and Comparison of Feature Selecting Algorithms
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- 발행일
- 200906
- 출처
- 한국멀티미디어학회지, v.12 no.6, pp.1-9
- ISSN
- 1229-778X
- 출판사
- 한국멀티미디어학회
- 협약과제
- 09MS3600, 정보투명성 보장형 디지털 포렌식 시스템 개발, 홍도원
- 초록
- More and richer information sources and services are available on the web everyday. However, harmful information, such as adult content, is not appropriate for all users, notably children. Since internet is a worldwide open network, it has a limit to regulate users providing harmful contents through each countrie’s national laws or systems. Additionally it is not a desirable way of developing a certain system-specific classification technology for harmful contents, because internet users can contact with them in diverse ways, for example, porn sites, harmful spams, or peer-to-peer networks, etc. Therefore, it is being emphasized to research and develop context-based core technologies for classifying harmful contents. In this paper, we propose an efficient text filter for blocking harmful texts of web documents using context-based technologies and examine which algorithms for feature selection, the process that select content terms, as features, can be useful for text categorization in all content term occurs in documents, are suitable for classifying harmful contents through implementation and experiment.
- KSP 제안 키워드
- Context-based, Feature selection(FS), Harmful information, Information sources, Peer-to-Peer(P2P), Text categorization, Web Documents, internet users, open network