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Feature Based Rule Learner in Noisy Environment Using Neighbourhood Rough Set Model
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Author(s): Yang Liu (Xi’an Jiaotong University, China), Luyang Jiao (First Affiliated Hospital of Xinxiang Medical College, China), Guohua Bai (Blekinge Institute of Technology, Sweden)and Boqin Feng (Xi’an Jiaotong University, China)
Copyright: 2012
Pages: 19
Source title:
Breakthroughs in Software Science and Computational Intelligence
Source Author(s)/Editor(s): Yingxu Wang (University of Calgary, Canada)
DOI: 10.4018/978-1-4666-0264-9.ch004
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Abstract
From the perspective of cognitive informatics, cognition can be viewed as the acquisition of knowledge. In real-world applications, information systems usually contain some degree of noisy data. A new model proposed to deal with the hybrid-feature selection problem combines the neighbourhood approximation and variable precision rough set models. Then rule induction algorithm can learn from selected features in order to reduce the complexity of rule sets. Through proposed integration, the knowledge acquisition process becomes insensitive to the dimensionality of data with a pre-defined tolerance degree of noise and uncertainty for misclassification. When the authors apply the method to a Chinese diabetic diagnosis problem, the hybrid-attribute reduction method selected only five attributes from totally thirty-four measurements. Rule learner produced eight rules with average two attributes in the left part of an IF-THEN rule form, which is a manageable set of rules. The demonstrated experiment shows that the present approach is effective in handling real-world problems.
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