Show simple item record Wang, Xiangyang Yang, Jie Jensen, Richard Liu, Xiaojun 2008-01-15T15:02:06Z 2008-01-15T15:02:06Z 2006-08-08
dc.identifier.citation Wang , X , Yang , J , Jensen , R & Liu , X 2006 , ' Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma ' Computer Methods and Programs in Biomedicine , vol 83 , no. 2 , pp. 147-156 . DOI: 10.1016/j.cmpb.2006.06.007 en
dc.identifier.issn 0169-2607
dc.identifier.other PURE: 73980
dc.identifier.other PURE UUID: d24e5ac2-c2ef-4368-a64c-92e61080b868
dc.identifier.other dspace: 2160/422
dc.identifier.other DSpace_20121128.csv: row: 307
dc.identifier.other Scopus: 33746881122
dc.identifier.other PubMed: 16893588
dc.identifier.uri en
dc.description X. Wang, J. Yang, R. Jensen and X. Liu, 'Rough Set Feature Selection and Rule Induction for Prediction of Malignancy Degree in Brain Glioma,' Computer Methods and Programs in Biomedicine, vol. 83, no. 2, pp. 147-156, 2006. en
dc.description.abstract The degree of malignancy in brain glioma is assessed based on Magnetic Resonance Imaging (MRI) findings and clinical data before operation. These data contain irrelevant features, while uncertainties and missing values also exist. Rough set theory can deal with vagueness and uncertainty in data analysis, and can efficiently remove redundant information. In this paper, a rough set method is applied to predict the degree of malignancy. As feature selection can improve the classification accuracy effectively, rough set feature selection algorithms are employed to select features. The selected feature subsets are used to generate decision rules for the classification task. A rough set attribute reduction algorithm that employs a search method based on Particle Swarm Optimization (PSO) is proposed in this paper and compared with other rough set reduction algorithms. Experimental results show that reducts found by the proposed algorithm are more efficient and can generate decision rules with better classification performance. The rough set rule-based method can achieve higher classification accuracy than other intelligent analysis methods such as neural networks, decision trees and a fuzzy rule extraction algorithm based on Fuzzy Min-Max Neural Networks (FRE-FMMNN). Moreover, the decision rules induced by rough set rule induction algorithm can reveal regular and interpretable patterns of the relations between glioma MRI features and the degree of malignancy, which are helpful for medical experts. en
dc.format.extent 10 en
dc.language.iso eng
dc.relation.ispartof Computer Methods and Programs in Biomedicine en
dc.rights en
dc.subject brain glioma en
dc.subject degree of malignancy en
dc.subject rough sets en
dc.subject feature selection en
dc.subject particle swarm optimization (PSO) en
dc.title Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Peer reviewed en

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