Earticle

초록

영어

A document classifier is an essential tool for classifying the various types of documents being generated in the Big Data era. In recent years, the wide variety of information services available for use with smartphones and portable mobile devices (tablets) have provided a technique that efficiently classifies the quality of sorted data. A common type of document classification scheme is the naïve Bayes classifier. The Naïve Bayes scheme is based on performance classification, which varies widely depending on the method of extraction used in the document. In this paper, we propose a system model that offers feature extraction methods which combine frequency with associated words. This model is then applied to the Naïve Bayes classifier to precisely classify documents. This method is proposed as an alternative to using traditional classification techniques. In addition, experiments will be evaluated by the existing document classification techniques and the proposed techniques.

목차

Abstract
 1. Introduction
 2. Related Works
  2.1. A Term Frequency-Inverse Document Frequency (TF-IDF) [5-7]
  2.2. Naïve Bayes Classifier [8-9]
  2.3. Apriori [10-12]
 3. Improving Feature Extraction [13]
 4. System Model [13]
  4.1. Morphological Analysis and Feature Extraction
  4.2. Document Classification
 5. Experiments and Considerations
 6. Conclusion
 Acknowledgments
 References

키워드

저자

• Jong-Yeol Yoo [ Dept. of Information & Communications Engineering, Daejeon University, Daejeon, Korea ]
• Min-Ho Lee [ Dept. of Information & Communications Engineering, Daejeon University, Daejeon, Korea ]
• Grace Aloyce [ Dept. of Information & Communications Engineering, Daejeon University, Daejeon, Korea ]
• Dong-Min Yang [ Dept. of Information & Communications Engineering, Daejeon University, Daejeon, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605