Network and Computer Centre, National Chiao Tung University, Hsinchu, Taiwan.
J Med Syst. 2012 Oct;36(5):2841-7. doi: 10.1007/s10916-011-9762-6. Epub 2011 Aug 3.
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.
本文对医学诊断数据中的乳腺癌进行分类。采用信息增益进行特征选择。针对分类问题，开发了神经模糊（NF）、k-最近邻（KNN）、二次分类器（QC）等单一模型方案及其相关的集成模型方案。此外，还构建了一个结合这三种方案的组合集成模型进行进一步验证。实验结果表明，集成学习的性能优于单个模型。此外，组合集成模型在所有模型中对乳腺癌的分类准确率最高。
