Earticle

초록

영어

Document classification is a growing interest in the research of text mining. Correctly identifying the documents into particular category is still presenting challenge because of large and vast amount of features in the dataset. In regards to the existing classifying approaches, Naïve Bayes is potentially good at serving as a document classification model due to its simplicity. The aim of this paper is to highlight the performance of employing Naïve Bayes in document classification. Results show that Naïve Bayes is the best classifiers against several common classifiers (such as decision tree, neural network, and support vector machines) in term of accuracy and computational efficiency.

목차

Abstract
 1. Introduction
 2. Research Methodology
  2.1. Phase 1: Preprocessing
  2.2. Phase 2: Feature Selection
  2.3. Phase 3: Adoption of Document Classifier – Naïve Bayes
  2.4. Phase 4: Model Evaluation
 3. Performance Evaluation
  3.1. Data Description
  3.2. Experimental Results and Discussions
 4. Conclusions
 References

키워드

저자

• S.L. Ting [ Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hum, Kowloon, Hong Kong ]
• W.H. Ip [ Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hum, Kowloon, Hong Kong ]
• Albert H.C. Tsang [ Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hum, Kowloon, Hong Kong ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605