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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >A Model of High-Dimensional Feature Reduction Based on Variable Precision Rough Set and Genetic Algorithm in Medical Image
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A Model of High-Dimensional Feature Reduction Based on Variable Precision Rough Set and Genetic Algorithm in Medical Image

机译：A Model of High-Dimensional Feature Reduction Based on Variable Precision Rough Set and Genetic Algorithm in Medical Image

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摘要

Aiming at the shortcomings of high feature reduction using traditional rough sets, such as insensitivity with noise data and easy loss of potentially useful information, combining with genetic algorithm, in this paper, a VPRS-GA (Variable Precision Rough Set--Genetic Algorithm) model for high-dimensional feature reduction of medical image is proposed. Firstly, rigid inclusion of the lower approximation is extended to partial inclusion by classification error rate beta in the traditional rough set model, and the ability dealing with noise data is improved. Secondly, some factors of feature reduction are considered, such as attribute dependency, attributes reduction length, and gene coding weight. A general framework of fitness function is put forward, and different fitness functions are constructed by using different factors such as weight and classification error rate beta. Finally, 98 dimensional features of PET/CT lung tumor ROI are extracted to build decision information table of lung tumor patients. Three kinds of experiments in high-dimensional feature reduction are carried out, using support vector machine to verify the influence of recognition accuracy in different fitness function parameters and classification error rate. Experimental results show that classification accuracy is affected deeply by different weight values under the invariable classification error rate condition and by increasing classification error rate under the invariable weigh value condition. Hence, in order to achieve better recognition accuracy, different problems use suitable parameter combination.

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    Tao Zhou; Lu Huiling; Hu FuyuanQiu ShiWu Cuiying;

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    North Minzu Univ, Sch Comp Sci & Engn, Yinchuan 750021, Ningxia, Peoples R China|Ningxia Med Univ, Sch Sci, Yinchuan 750004, Ningxia, Peoples R China;

    Ningxia Med Univ, Sch Sci, Yinchuan 750004, Ningxia, Peoples R China;

    Suzhou Univ Sci & Technol, Sch Elect & Informat Engn, Suzhou 215009, Peoples R ChinaChinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging Technol CAS, Xian 710119, Peoples R ChinaXiamen Med Coll, Human Resources Dept, Affiliated Hosp 2, Xiamen 361021, Peoples R China;

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