Using rough set to induce dependencies between attributes where there are a large amount of missing values: a SARS data application

Authors:
Feng Honghai;Liu Baoyan;He Liyun
Affiliations:
University of Science and Technology Beijing, China and Hebei Agricultural University, Baoding, China;China Academy of Traditional Chinese Medicine, Beijing, China;China Academy of Traditional Chinese Medicine, Beijing, China
Venue:
Proceedings of the 3rd international conference on Knowledge capture
Year:
2005

Citing 2
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The nature of statistical learning theory

The nature of statistical learning theory
Rough Sets: Theoretical Aspects of Reasoning about Data

Rough Sets: Theoretical Aspects of Reasoning about Data

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Abstract

Because of the amount of missing values in our SARS data set is very large, to fill in them wholly with the existing methods is impossible or the results of being filled in are not reliable. Only taking two attributes into account can avoid using the large amount of missing values, which is the feature of rough set that other machine learning method cannot hold. In this paper, we induced some rules based on rough set from the SARS data set that have not been detected by medical experts in clinic practice.