Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma

Xiangyang Wang, Jie Yang, Richard Jensen, Xiaojun Liu

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

83 Dyfyniadau(SciVal)
93 Wedi eu Llwytho i Lawr (Pure)


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.
Iaith wreiddiolSaesneg
Tudalennau (o-i)147-156
Nifer y tudalennau10
CyfnodolynComputer Methods and Programs in Biomedicine
Rhif cyhoeddi2
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 08 Awst 2006

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