Improved Binary Imperialist Competition Algorithm for Feature Selection from Gene Expression Data
Improved Binary Imperialist Competition Algorithm for Feature Selection from Gene Expression Data
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DOI：
10.1007/978-3-319-42297-8_7
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发表时间：
2016-08
期刊：
影响因子：
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通讯作者：
Aorigele Bao;Shuaiqun Wang;Zheng Tang;Shangce Gao;Yuki Todo
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文献类型：
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作者：
Aorigele Bao;Shuaiqun Wang;Zheng Tang;Shangce Gao;Yuki Todo
在分子水平上代表细胞状态的基因表达谱可能在分类平台和熟练癌症诊断的进展中很重要。本文尝试利用帝国主义竞争算法（伊卡）的并行计算和更快的收敛速度来选择最少的信息基因。然而，伊卡算法和其他进化算法一样，容易陷入局部最优.为了避免这一缺陷，本文提出了一种改进的二进制伊卡（IBICA），其思想是：当帝国中的局部最佳城市（帝国主义者）的适应值在连续5次迭代后不变时，将其重置为零。然后IBICA经验性地应用于一套著名的基准基因表达数据集。实验结果表明，该方法的分类精度和选择基因的数量均优于其他相关工作的上级。
Gene expression profiles which represent the state of a cell at a molecular level could likely be important in the progress of classification platforms and proficient cancer diagnoses. In this paper, we attempt to apply imperialist competition algorithm (ICA) with parallel computation and faster convergence speed to select the least number of informative genes. However, ICA same as the other evolutionary algorithms is easy to fall into local optimum. In order to avoid the defect, we propose an improved binary ICA (IBICA) with the idea that the local best city (imperialist) in an empire is reset to the zero position when its fitness value does not change after five consecutive iterations. Then IBICA is empirically applied to a suite of well-known benchmark gene expression datasets. Experimental results show that the classification accuracy and the number of selected genes are superior to other previous related works.