원문정보
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초록
영어
After analysis the importance of continuous attributes in the C5.0 algorithm processing, and consider the shortcomings of discretization method of C5 algorithm, this paper proposes a new method based on Rough set theory- information entropy- discernible matrix discretization (RSIEDM). The method uses rough, information entropy and Discernible matrix that can be more reasonable and more accurately to continuously attribute discretization, and making created decision tree have better accuracy. In the application of optimization of lightning disaster statistics and evaluation result of lightning disaster, the algorithm which has obtained a better effect.
목차
Abstract
1. Introduction
2. Decision Tree Classification Algorithm
2.1 Create Decision Tree
2.2 Discretize the continuous valued attribute
3. Design Discretized Research based on Rough Set-information Entropy-Discernible Matrix
3.1 Algorithm of initial breakpoints set
3.2 The information entropy of rough set attributes discretization method
3.3 Attribute reduction of discernibility matrix
4. Body Area Sensor Network
5. Conclusion
Acknowledgements
References
1. Introduction
2. Decision Tree Classification Algorithm
2.1 Create Decision Tree
2.2 Discretize the continuous valued attribute
3. Design Discretized Research based on Rough Set-information Entropy-Discernible Matrix
3.1 Algorithm of initial breakpoints set
3.2 The information entropy of rough set attributes discretization method
3.3 Attribute reduction of discernibility matrix
4. Body Area Sensor Network
5. Conclusion
Acknowledgements
References
저자정보
참고문헌
자료제공 : 네이버학술정보