计算机应用与软件2012，Vol.29Issue(2)：254-257,4.
一种新的基于属性相关性的数据流特征选择算法的研究
A NEW DATA STREAM FEATURE SELECTION ALGORITHM BASED ON ATTRIBUTE RELEVANCE
陈万松 1赵雷1
作者信息
- 1. 苏州大学计算机科学与技术学院 江苏苏州215006
- 折叠
摘要
Abstract
High dimensional data stream contains a lot of irrelevant and redundant information, which may greatly downgrade the performance of learning algorithms. With attribute relevance, the irrelevant and redundant attributes can be effectively removed. As a result, the efficiency of learning algorithms can be improved. The paper analyzes the limitations of existing attribute relevance calculation methods and proposes an attribute relevance feature selection algorithm based on curve-fitting, called Feature Selection based on Curve-Fitting Feature Relevance (FSCFFR). Both theoretical analysis and experiments have illustrated that FSCFFR is more real-time and more effective during the feature selection process.关键词
数据流/特征选择/属性相关性Key words
Data stream/Feature selection/Attribute relevance分类
信息技术与安全科学引用本文复制引用
陈万松,赵雷..一种新的基于属性相关性的数据流特征选择算法的研究[J].计算机应用与软件,2012,29(2):254-257,4.
