基于鱼群优化算法和Cholesky分解的RELM的基因表达数据分类

Improved RELM Based on Fish Swarm Optimization Algorithm and Cholesky Decomposition for Gene Expression Data Classification

提出一种基于鱼群优化算法和Cholesky分解的改进的正则极限学习机算法(FSC-RELM)来对基因表达数据进行分类。FSC-RELM算法中,首先用鱼群优化算法对RELM输入层权值进行优化,其中目标函数定义为误差函数的倒数;再对RELM输出层权值矩阵进行分解,采用Cholesky分解法进行优化,以提高算法速度,减少训练时间。为了评价算法性能,对若干标准基因数据集进行了实验,结果表明,FSC-RELM算法在较短的时间内可以获得较高的分类精度,性能优异。

The paper proposed an improved algorithm of regular extreme learning machine（FSC-RELM） based on fish swarm optimization algorithm and Cholesky decomposition to apply in classification of gene expression data.Firstly,fish swarm optimization algorithm is used to optimize the weights of input layer and the value of objective function is defined as the reciprocal of error function.For improving the speed of the algorithm and reducing the training time,Cholesky decomposition is used on RELM output layer weights matrix.The experiments on the standard genetic data sets show that the FSC-RELM algorithm in a relatively short period of time can obtain higher classification accuracy and good performance.