FEATURE REDUCTION OF FLOW PATTERN IDENTIFICATION BY INTERACTION INDEX

摘要：

This paper addresses the feature reduction problem in the flow pattern identification. Classification is often performed in the flow pattern identification based on data extraction from all designed instruments. However, the raw data may contain a large number of features, but many features are not necessary, even resulting in more imprecise identification. The classifier also is difficult to be determined when the number of training data needed to design a classifier grows with the number of the features. Hence, a way is needed to reduce the number of the features without losing any essential information. Data fusion method is helpful to realize feature reduction. This paper presents a new method for feature reduction using interaction index-based data fusion, and compares it with some methods presented earlier in the literature. This new method has higher correctness rate and more predictable performances than the same type of other methods.

关键词： Data Fusion Feature Reduction Classification Flow Pattern

作者: SHI-HONG YUE BEI-BEI LI JING WANG

作者单位: School of Automation, Tianjin University, Tianjin 300072, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 411-416

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）