以演化徑向基函網路進行癌症分類之研究

Abstract

本文對於使用微矩陣資料進行癌症分類提供一種新的方法，演化徑向基函網路（Evolutionary Radial Basis Function Network - ERBFN）。演化徑向基底函數網路是普通的徑向基底函數網路的一個顯著的改進。ERBFN從傳統的分群演算法開始, 接著對Radial Basis Function Network的隱藏層進行最佳化，並且使用監督式學習策略調整網路連接的加權值。目前這個方法也已經成功的應用於真實的癌症資料的分類上。根據我們的評估，ERBFN的正確性可以跟已SVM為基礎的分類相比較。

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.