[1]韩 笑,毕 波,唐锦萍,等.核零空间方法在乳腺癌异常检测中的应用[J].计算机技术与发展,2022,32(01):165-169.[doi:10. 3969 / j. issn. 1673-629X. 2022. 01. 028]
　HAN Xiao,BI Bo,TANG Jin-ping,et al.Application of Kernel Null Space Method in Breast CancerAbnormal Detection[J].,2022,32(01):165-169.[doi:10. 3969 / j. issn. 1673-629X. 2022. 01. 028]
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核零空间方法在乳腺癌异常检测中的应用()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年01期
页码:
165-169
栏目:
应用前沿与综合
出版日期:
2022-01-10

文章信息/Info

Title:
Application of Kernel Null Space Method in Breast CancerAbnormal Detection
文章编号:
1673-629X(2022)01--0165-05
作者:
韩 笑1 ; 毕 波1; 2 ; 唐锦萍3 ; 曹 莉2
1. 东北石油大学 数学与统计学院,黑龙江 大庆 163318;
2. 海南医学院公共卫生学院,海南 海口 571101;
3. 黑龙江大学 数据科学与技术学院,黑龙江 哈尔滨 150080
Author(s):
HAN Xiao1 ; BI Bo1; 2 ; TANG Jin-ping3 ; CAO Li2
1. School of Mathematics and Statistics,Northeast Petroleum University,Daqing 163318,China;
2. School of Public Health,Hainan Medical College,Haikou 571101,China;
3. School of Data Science and Technology,Heilongjiang University,Harbin 150080,China
关键词:
乳腺癌; 异常检测; 核零空间算法; 核函数; 异常阈值
Keywords:
breast cancer; abnormal detection; kernel null space method; kernel function; abnormal threshold
分类号:
TP39
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 01. 028
摘要:
当今时代,乳腺癌越来越成为了女性的高发病,因此尽早地排除异常因素,进行对症治疗,可以大大降低疾病风险。 考虑到乳腺癌数据特征比较多,并且往往不仅存在线性特征还隐含着很多非线性特征,针对这一问题提出利用核零空间算法来进行乳腺癌的异常检测。 首先利用核函数将所有的正常样本进行非线性映射变换到高维空间,再通过零空间变换将类内散度转换为 0,并且将零空间中整个类的数据用该类的平均值代替,最后通过计算测试样本到该值的距离判断测试样本的异常性。 该算法大大降低了计算的复杂性,也提高了乳腺癌检测的速度。 通过在 UCI 乳腺癌数据库上的仿真实验,并对不同核函数以及设定的不同异常阈值下得到的 F1-score 进行对比,发现在不同核函数以及不同异常阈值下的结果是不同的,且在选取高斯核作为核函数时,可使得 F1-score 结果达到 0. 962 7。 充分证明了将核零空间算法用于乳腺癌异常检测是有效的。
Abstract:
Nowadays,breast cancer has increasingly become a high incidence of women. Therefore,removing abnormal factors as soon aspossible and conducting diagnosis and treatment can greatly reduce the risk of disease. Considering that there are many features in breastcancer data,and there are often not only linear features but also many non-linear features. To solve this problem,a kernel null space algorithm is proposed to detect abnormalities of breast cancer. Firstly,the kernel function is adopted to perform nonlinear mapping andtransformation of all normal samples into high-dimensional space. Secondly,the intra-class divergence is converted to 0 through zerospace transformation,and the data of the entire class in the zero space is replaced with the average value of the class. Finally, theabnormality of the test sample is judged by calculating the distance from the test sample to the value. The proposed algorithm greatlyreduces the complexity of the calculation and also improves the speed of breast cancer detection. Through simulation experiments on theUCI breast cancer database,the F1-score obtained under different kernel functions and different set abnormal thresholds is compared. It isfound that the results under different kernel functions and different abnormal thresholds are different,and when the Gaussian kernel isselected as the kernel function,the F1 -score can reach 0. 962 7,which fully proves that the kernel null space algorithm is effective inbreast cancer abnormality detection.

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更新日期/Last Update: 2022-01-10