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Design ensemble machine learning model for breast cancer diagnosis.

机译:设计用于乳腺癌诊断的集成机器学习模型。

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摘要

In this paper, we classify the breast cancer of medical diagnostic data. Information gain has been adapted for feature selections. Neural fuzzy (NF), k-nearest neighbor (KNN), quadratic classifier (QC), each single model scheme as well as their associated, ensemble ones have been developed for classifications. In addition, a combined ensemble model with these three schemes has been constructed for further validations. The experimental results indicate that the ensemble learning performs better than individual single ones. Moreover, the combined ensemble model illustrates the highest accuracy of classifications for the breast cancer among all models.
机译:在本文中,我们对乳腺癌的医学诊断数据进行了分类。信息增益已针对特征选择进行了调整。已经开发了神经模糊(NF),k最近邻(KNN),二次分类器(QC),每个单独的模型方案及其关联的整体模型进行分类。另外,已经构建了具有这三种方案的组合集成模型以用于进一步的验证。实验结果表明,集成学习的效果要优于单个单独的集成学习。此外,组合集成模型说明了所有模型中乳腺癌分类的最高准确性。

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