Classification of Microarray Data Using Evolutionary Hypernetworks with Subspace Fusion

WANG Jin; LIU Bin; ZHANG Jun; CHEN Qiao-song; DENG Xin

doi:10.3969/j.issn.0372-2112.2016.10.004

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Classification of Microarray Data Using Evolutionary Hypernetworks with Subspace Fusion

更新时间：2025-07-16

- Classification of Microarray Data Using Evolutionary Hypernetworks with Subspace Fusion
- Acta Electronica Sinica Vol. 44, Issue 10, Pages: 2308-2313(2016)
- 作者机构：
  
  重庆邮电大学计算智能重庆市重点实验室,重庆,400065
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2016.10.004
  CLC： TP39
- Published：2016
- 稿件说明：
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WANG Jin, LIU Bin, ZHANG Jun, et al. Classification of Microarray Data Using Evolutionary Hypernetworks with Subspace Fusion[J]. Acta Electronica Sinica, 2016, 44(10): 2308-2313.
DOI：

WANG Jin, LIU Bin, ZHANG Jun, et al. Classification of Microarray Data Using Evolutionary Hypernetworks with Subspace Fusion[J]. Acta Electronica Sinica, 2016, 44(10): 2308-2313. DOI： 10.3969/j.issn.0372-2112.2016.10.004.

摘要

针对传统模式识别方法在学习具有小样本特性的DNA微阵列数据时存在的过拟合问题，本文提出了一种子空间融合演化超网络模型.该模型通过子空间划分、超边全覆盖和子空间融合三种方法降低模型对初始化的依赖，减少了对数据空间的拟合误差，提高了演化超网络的泛化能力.对四个DNA微阵列数据集的实验结果表明，子空间融合演化超网络的识别率和在小样本训练集下的泛化能力均优于参与对比的其他传统模式识别方法.

Abstract

In order to solve the over-fitting problem of the traditional pattern recognition approaches under the DNA microarray data with small train samples

a subspace fusion-based evolutionary hypernetwork model is proposed in this paper.With the methods of subspace division

hyperedge coverage

and subspace fusion

the proposed scheme reduces the dependence on the initialization

decreases the fitting error of the data space

and enhances the generalization ability of the evolutionary hypernetwork.The experimental results on four DNA microarray datasets show that the proposed model achieves higher classification accuracy and stronger generalization ability than other compared traditional pattern recognition method.

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