关键词: 特征融合, 熵密度, 自相关系数, 线性判别分析（LDA）, 支持向量机

Abstract: Based on feature fusion, combining amino acid composition, entropy density and autocorrelation coefficient to construct a 190 dimensional eigenvector for characteristic expression, this method can better express the protein structure information compared with the traditional method which only considers the amino acid composition information. It uses the  Linear Discriminant Analysis（LDA） method to reduce the calculation complexity and increases the correlation between the samples. The support vector machine is selected as the classifier for positioning prediction. It uses the Jackknife method to cross-check the gram-negative and gram-positive data sets. The experimental results show that the multi-feature combination method is superior to the traditional amino acid composition method and simple self-correlation coefficient method, and proves the validity of the new method.

Key words: feature fusion, entropy density, autocorrelation coefficient, Linear Discriminant Analysis（LDA）, support vector machine