A Combination of Rough-Based Feature Selection and RBF Neural Network for Classification Using Gene Expression Data

Chiang* J.-H.; Ho S.-H.

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A Combination of Rough-Based Feature Selection and RBF Neural Network for Classification Using Gene Expression Data

机译：基于粗糙集的特征选择和RBF神经网络的基因表达数据分类

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This paper presents a novel rough-based feature selection method for gene expression data analysis. It can find the relevant features without requiring the number of clusters to be known a priori and identify the centers that approximate to the correct ones. In this paper, we attempt to introduce a prediction scheme that combines the rough-based feature selection method with radial basis function neural network. For further consider the effect of different feature selection methods and classifiers on this prediction process, we use the NaÏve Bayes and linear support vector machine as classifiers, and compare the performance with other feature selection methods, including information gain and principle component analysis. We demonstrate the performance by several published datasets and the results show that our proposed method can achieve high classification accuracy rate.

机译：本文提出了一种新的基于粗略特征选择的基因表达数据分析方法。它可以找到相关特征，而无需先验地知道聚类的数量，并识别近似于正确聚类的中心。在本文中，我们尝试引入一种结合了基于粗糙特征选择方法和径向基函数神经网络的预测方案。为了进一步考虑不同特征选择方法和分类器对该预测过程的影响，我们使用Naveve Bayes和线性支持向量机作为分类器，并将其性能与其他特征选择方法（包括信息增益和主成分分析）进行比较。我们通过几个已公开的数据集证明了该方法的性能，结果表明我们提出的方法可以达到较高的分类准确率。

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来源
《IEEE transactions on nanobioscience》 |2008年第1期|p.91-99|共9页
作者
Chiang* J.-H.; Ho S.-H.;
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正文语种 eng
中图分类特种结构材料;
关键词
Information gain (IG); microarray data; principle component analysis; radial basis function (RBF) neural network; rough-based feature selection;

机译：信息增益（IG）;微阵列数据;原理成分分析;径向基函数（RBF）神经网络;基于粗糙的特征选择;

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专利

1. A combinational feature selection and ensemble neural network method for classification of gene expression data [J] . Bing Liu, Qinghua Cui, Tianzi Jiang, BMC Bioinformatics . 2004,第1期

机译：特征选择与集成神经网络的基因表达数据分类方法
2. Gene Expression Dataset Classification Using Artificial Neural Network and Clustering-Based Feature Selection [J] . International journal of swarm intelligence research . 2020,第1期

机译：基于人工神经网络和聚类特征选择的基因表达数据集分类
3. Feature selection of gene expression data for Cancer classification using double RBF-kernels [J] . Shenghui Liu, Chunrui Xu, Yusen Zhang, BMC Bioinformatics . 2018,第1期

机译：使用Double RBF-rernels进行癌症分类基因表达数据的特征选择
4. Applying RBF Neural Networks to Cancer Classification Based on Gene Expressions [C] . Feng Chu, Lipo Wang . -1

机译：基于基因表达的RBF神经网络在癌症分类中的应用
5. Feature selection of microarray data using genetic algorithms and artificial neural networks. [D] . Yacci, Paul. 2009

机译：使用遗传算法和人工神经网络对微阵列数据进行特征选择。
6. A combinational feature selection and ensemble neural network method for classification of gene expression data [O] . Bing Liu, Qinghua Cui, Tianzi Jiang, 2004

机译：基因表达数据分类的组合特征选择和集成神经网络方法
7. A combinational feature selection and ensemble neural network method for classification of gene expression data [O] . Jiang Tianzi, Cui Qinghua, Liu Bing, 2004

机译：基因表达数据分类的组合特征选择和集成神经网络方法
8. Salient Feature Selection Using Feed-Forward Neural Networks and Signal- to-Noise Ratios with a Focus Toward Network Threat Detection and Classification. [R] . K. L. Moore 2014

机译：使用前馈神经网络和信噪比的突出特征选择，重点是网络威胁检测和分类。

A Combination of Rough-Based Feature Selection and RBF Neural Network for Classification Using Gene Expression Data

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