Improving the genetic bee colony optimization algorithm for efficient gene selection in microarray data
Improving the genetic bee colony optimization algorithm for efficient gene selection in microarray data
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DOI：
10.1007/s13748-018-0161-9
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发表时间：
2018-12-01
影响因子：
4.2
通讯作者：
Velazquez-Rodriguez, Camilo
中科院分区：
文献类型：
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作者：
Angulo, Adrian Pino;Shin, Kilho;Velazquez-Rodriguez, Camilo
特征选择是生物医学数据挖掘应用程序工作流程中非常关键的组成部分。特别是，需要能够找到基因之间复杂关系且计算效率高的特征选择方法。在微阵列数据分析范围内，遗传蜂群（GBC）算法是最好的特征选择算法之一，它利用遗传和蚁群优化算法的结合来搜索最优解。在本文中，我们深入分析了 GBC 背后的基本原理，并提出了一些效率和准确性方面的改进，以便研究人员可以更好地利用这种优秀的方法。通过（i）用更有效的技术取代GBC的过滤阶段，（ii）改进GBC中使用的人工菌落算法中的群体生成，以及（iii）改进GBC中的开发方法，我们在微阵列数据集上的实验表明，我们的新方法GBC+不仅明显更准确，而且平均比原始方法快十倍左右
Feature selection is a very critical component in the workflow of biomedical data mining applications. In particular, there is a need for feature selection methods that can find complex relationships among genes, yet computationally efficient. Within the scope of microarray data analysis, the genetic bee colony (Gbc) algorithm is one of the best feature selection algorithms, which leverages the combination between genetic and ant colony optimization algorithms to search for the optimal solution. In this paper, we analyse in depth the fundamentals lying behind the Gbc and propose some improvements in both efficiency and accuracy, so that researchers can even take more advantage of this excellent method. By (i) replacing the filtering phase of Gbc with a more efficient technique, (ii) improving the population generation in the artificial colony algorithm used in Gbc, and (iii) improving the exploitation method in Gbc, our experiments in microarray data sets reveal that our new method Gbc+ is not only significantly more accurate, but also around ten times faster on average than the original