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

来源 :自动化应用 | 被引量 : 0次 | 上传用户：zhuxin1109

【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.

【作者】

：

黄经纬

【机构】

：

江西理工大学电气工程与自动化学院,江西赣州 341000

【出处】

：

自动化应用

【发表日期】

：

2021年10期

【关键词】

：

样本扩充特征融合肿瘤基因表达谱分类数据

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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

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