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The classification of breast cancer with Machine Learning Techniques

机译:机器学习技术对乳腺癌的分类

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In this study, it is aimed to classify breast cancer data attained from UCI(University of California-Irvine), Machine Learning Laboratory with some Machine Learning Techniques. With this aim, clustering performance of some distance measures in Matlab¿¿ has been compared, using breast cancer data. Later without using any pre-processing, some of the machine learning techniques are used for the clustering breast cancer data, using WEKA data mining software¿¿. As a result, it has been seen that distance measures effects the clustering performance nearly 12 percentage and the success of the classification varies from %45 to %79, according to the methods.
机译:在这项研究中,其目的是通过一些机器学习技术对从UCI(加利福尼亚大学欧文分校)的机器学习实验室获得的乳腺癌数据进行分类。出于这个目的,已经使用乳腺癌数据比较了Matlab中某些距离度量的聚类性能。后来,无需使用任何预处理,便使用WEKA数据挖掘软件将某些机器学习技术用于聚类乳腺癌数据。结果表明,根据这些方法,距离度量会影响聚类性能近12%,并且分类成功率从%45到%79不等。

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