.png){kind=icon}
返回
ROUGH SUPPORT VECTOR MACHINE FOR CLASSIFICATION WITH INTERVAL AND INCOMPLETE DATA
.png){kind=icon}
OA
AI
DOI：10.2478/jaiscr-2020-0004.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The paper presents the idea of connecting the concepts of the Vapnik's support vector machine with Pawlak's rough sets in one classification scheme. The hybrid system will be applied to classifying data in the form of intervals and with missing values [1]. Both situations will be treated as a cause of dividing input space into equivalence classes. Then, the SVM procedure will lead to a classification of input data into rough sets of the desired classes, i.e. to their positive, boundary or negative regions. Such a form of answer is also called a three-way decision. The proposed solution will be tested using several popular benchmarks.
Keyword:
support vector machines
rough sets
missing features
interval data
three-way decision
期刊
IF:
2.4
论文数:
170
被引数:
459
机构
引用论文
.png){kind=icon}
分享收藏A dynamic programming approach to missing data estimation using neural networks使用神经网络进行缺失数据估计的动态规划方法
PREAI
分享收藏
查看更多内容