원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
Vol.9 No.11
2016.11
pp.61-70
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
CHI statistical text feature selection method based on information entropy optimization is presented in this paper. In the text categorization process of feature selection, considering the results of effect of the distribution within categories and among categories, we introduce the frequency of features information entropy among categories, the information entropy within categories, information within category to optimize the CHI statistical methods. The experimental results show that the classification accuracy of the optimized CHI method is significantly higher than that the traditional CHI statistical methods.
목차
Abstract
1. Introduction
2. CHI Statistics Algorithm Based on Information Entropy Optimization
2.1. Thought of Information Entropy among Categories
2.2. Thought of Information Entropy within a Category
2.3. Feature Frequency Information within a Category
2.4. CHI Statistical Algorithm is Based on Information Entropy
3. Experiments and Analysis
3.1. Dataset
3.2. Evaluation Criteria
3.3. Experiment Result
4. Conclusion
References
1. Introduction
2. CHI Statistics Algorithm Based on Information Entropy Optimization
2.1. Thought of Information Entropy among Categories
2.2. Thought of Information Entropy within a Category
2.3. Feature Frequency Information within a Category
2.4. CHI Statistical Algorithm is Based on Information Entropy
3. Experiments and Analysis
3.1. Dataset
3.2. Evaluation Criteria
3.3. Experiment Result
4. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보