Breast cancer distinguishing method based on gaussian kernel function fuzzy noncorrelation distinguishing conversion
A technology of non-correlation discrimination and Gaussian kernel function, which is applied in complex mathematical operations, electrical digital data processing, special data processing applications, etc., and can solve the problems of non-correlation discrimination conversion method difficulty and unsatisfactory processing effect
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[0046] Below just illustrate with respect to the inventive method:
[0047] Explanation of experimental data: The breast cancer diagnostic data set (WDBC, WisconsinDiagnostic Breast Cancer) in Wisconsin, USA comes from the UCI machine learning database:
[0048] http: / / www.ics.uci.edu / ~mlearn / MLRepository.html, the WDBC dataset contains 569 sample data of 30 features. These features were calculated from digital images of breast masses from fine needle aspiration. They describe cell nuclei with digital images. The database includes two types of data: benign breast mass data and malignant breast mass data. Among them, there were 357 benign breast masses and 212 malignant breast masses.
[0049] Step 1. Fuzzy processing of the breast cancer diagnosis dataset:
[0050] 1. Use the K-nearest neighbor method to obtain sample x k (x k Belonging to the K nearest neighbor samples of class j), then x k The fuzzy membership value is calculated according to the following rules:
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