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Feature Based Rule Learner in Noisy Environment Using Neighbourhood Rough Set Model

Feature Based Rule Learner in Noisy Environment Using Neighbourhood Rough Set Model

Yang Liu, Luyang Jiao, Guohua Bai, Boqin Feng
Copyright: © 2010 |Volume: 2 |Issue: 2 |Pages: 20
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781609604196|DOI: 10.4018/jssci.2010040104
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MLA

Liu, Yang, et al. "Feature Based Rule Learner in Noisy Environment Using Neighbourhood Rough Set Model." IJSSCI vol.2, no.2 2010: pp.66-85. http://doi.org/10.4018/jssci.2010040104

APA

Liu, Y., Jiao, L., Bai, G., & Feng, B. (2010). Feature Based Rule Learner in Noisy Environment Using Neighbourhood Rough Set Model. International Journal of Software Science and Computational Intelligence (IJSSCI), 2(2), 66-85. http://doi.org/10.4018/jssci.2010040104

Chicago

Liu, Yang, et al. "Feature Based Rule Learner in Noisy Environment Using Neighbourhood Rough Set Model," International Journal of Software Science and Computational Intelligence (IJSSCI) 2, no.2: 66-85. http://doi.org/10.4018/jssci.2010040104

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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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