A combination of rough-based feature selection and RBF neural network for classification using gene expression data

Jung Hsien Chiang, Shing Hua Ho

研究成果: Article同行評審

48 引文 斯高帕斯(Scopus)

摘要

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 Naive 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.

原文English
頁(從 - 到)91-99
頁數9
期刊IEEE Transactions on Nanobioscience
7
發行號1
DOIs
出版狀態Published - 2008 三月

All Science Journal Classification (ASJC) codes

  • 生物技術
  • 生物工程
  • 醫藥(雜項)
  • 生物醫學工程
  • 藥學科學
  • 電腦科學應用
  • 電氣與電子工程

指紋

深入研究「A combination of rough-based feature selection and RBF neural network for classification using gene expression data」主題。共同形成了獨特的指紋。

引用此