首页> 外文期刊>International journal of computer science and network security >Feature Selection and the Fusion-based Method for Enhancing the Classification Accuracy of SVM for Breast Cancer Detection
【24h】

Feature Selection and the Fusion-based Method for Enhancing the Classification Accuracy of SVM for Breast Cancer Detection

机译:用于增强SVM分类精度的特征选择和基于融合的方法

获取原文
获取外文期刊封面目录资料

摘要

Recently, breast cancer has become the second leading cause of death from cancer in women. Although most studies have reported that this form of cancer is preventable and many of the risks can be avoided in its early stages, most of the traditional methods of detecting and diagnosing cancer take place at a very late stage. The classification method is one of the data mining techniques used as a detection method in early stage detection for this type of cancer. Feature selection methods have a positive impact and significant enhancement when used with classification methods. They result in increasing the classification accuracy, since they select the important features of images or any data instances. The objective of this study is to investigate the potential bene?t of using the feature selection algorithm as a pre-processing stage for enhancing the classification accuracy of the support vector machine, and to propose a fusion scheme for selecting the best and related features for mammogram images. For this purpose, four feature selection algorithms were chosen, namely mutual information (MI), the statistical dependence measure, the relief-based algorithm and the correlation based algorithm. Extensive experiments have been performed using one of the benchmark datasets, that of the Mammographic Image Analysis Society (MIAS), to test the proposed method on two classes, benign and malignant masses. The results showed that our proposed method at (85 ? 15%) data splitting percentage has a classification accuracy of 75% and 93.75% and positive rate of 87.5% and 88.89% for the top seven and top five features, respectively.
机译:最近,乳腺癌已成为女性癌症死亡的第二个主要原因。虽然大多数研究报告说,这种形式的癌症是可预防的,但在其早期阶段可以避免许多风险,大多数传统的检测和诊断癌症方法都在一个非常晚期的阶段。分类方法是用作这种癌症早期检测中的检测方法的数据挖掘技术之一。在与分类方法一起使用时,特征选择方法具有积极的影响和显着的增强。它们导致增加分类准确性,因为它们选择了图像的重要特征或任何数据实例。本研究的目的是研究使用特征选择算法作为预处理阶段的潜在的BENEΔT,用于提高支持向量机的分类精度,并提出用于选择最佳和相关特征的融合方案乳房图像图像。为此目的,选择了四种特征选择算法,即互信息(MI),统计依赖量度,基于浮雕的算法和基于相关的算法。使用基准数据集之一,乳房X线图图像分析协会(MIAS)的一个进行了广泛的实验,以测试两个类,良性和恶性群众的提出方法。结果表明,我们在(85?15%)数据分裂百分比上的提出方法分别为75%和93.75%,阳性率分别为87.5%和88.89%,分别为前七个和五大特征。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号