Cancer Classification with Evolutional Radial Basis Function Network
Date Issued
2005
Date
2005
Author(s)
Li, Chien-Hung
DOI
zh-TW
Abstract
In this work, we proposed a novel method, Evolutionary Radial Basis Function Network (ERBFN), for classification of cancer types with microarray gene expression data. Evolutionary Radial Basis Function Network is a significant improvement over ordinary Radial Basis Function Network. Starting with traditional clustering algorithm, ERBFN optimized the hidden layer of Radial Function Network, and used supervised learning strategy to fine-tune the network connection weights. This method has been successfully applied to classification of real-world cancer data. Our assessment has revealed that the accuracy of ERBFN is comparable to that of support vector machine based classification.
Subjects
微陣列
癌症
徑向基底函數網路
Microarray
Cancer
RBF
SDGs
Type
thesis