원문정보
보안공학연구지원센터(IJSEIA)
International Journal of Software Engineering and Its Applications
Vol.5 No.3
2011.07
pp.37-46
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보