Data Cleansing Based on Mathematic Morphology

摘要：

In the field of bioinformatics and medical image understanding, data noise frequently occurs and deteriorates the classification performance, therefore an effective data cleansing mechanism in the training data is often regarded as one of the major steps in the real world inductive learning applications. In this paper the related work on dealing with data noise is firstly reviewed, and then based on the principle of mathematic morphology, the morphological data cleansing algorithms are proposed and two concrete morphological algorithms, DILATEDC and CLOSE-DC, are realized. The experiments which are arranged on 15 UCI datasets show that these morphological data cleansing algorithms can effectively improve the classification performance, comparing with other relative methods.

关键词： data noise data cleansing mathematic morphology

作者: Sheng TANG Si-ping CHEN

作者单位: Department of Biomedical Engineering Zhejiang University Hangzhou, China Department of Biomedical Engineering Shenzhen University Shenzhen, China

会议类型: 国际会议

会议名称: The 2nd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2008)(第二届生物信息与生物医学工程国际会议)

会议地点: 上海

会议语种:英文

页码: 755-758

在线出版日期: 2008-05-16（万方平台首次上网日期，不代表论文的发表时间）