基于样本扩充和特征融合自动编码机的肿瘤基因表达数据分类
Classification of Tumor Gene Expression Data Based on Sample Expansion and Feature Fusion Automatic Coding Machine
摘要
针对肿瘤基因数据的样本小、维度高特点,为解决小样本对分类准确率的影响,提出对样本进行扩充的方法;结合特征获取的方式不同,将主成分分析(PCA)、核主成分分析(KPCA)和非负矩阵(NMF)特征进行组合,再通过鲁棒性更强的堆栈自动编码器(SDAE)和Softmax进行分类。实验表明,经过合理的样本特征组合及小样本扩充能够有效提升分类效果。
出处
《自动化应用》
2021年第10期15-17,22,共4页
Automation Application
基金
江西省教育厅科技项目(GJJ180484)。
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共引文献4
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