Due to the presence of irrelevant or redundant data in microarray datasets, capturing potential patterns accurately and directly via existing models is difficult. Feature selection (FS) has become a necessary strategy to identify and screen out the most relevant attributes. However, the high dimensionality of microarray datasets poses a serious challenge to most existing FS algorithms. For this purpose, we propose a novel feature selection strategy in this paper, called IG-MBKH. A pre-screening method of feature ranking which is based on information gain (IG) and an improved binary krill herd (MBKH) algorithm are integrated in this strategy. When searching for feature subsets using MBKH, a hyperbolic tangent function, an adaptive transfer factor, and a chaos memory weight factor are introduced to facilitate a better searching the possible feature subsets. The results indicates that the IG-MBKH algorithm can achieve improvement in convergence, the number of features and classification accuracy when compared to the BKH, MBKH, and several newest algorithms. Furthermore, we evaluate the impact of different classifiers on the performance of the strategy we propose.

中文翻译：

---

使用混合信息增益和改进的二元磷虾群算法进行微阵列数据分类的特征选择。

由于微阵列数据集中不相关或冗余的数据的存在，很难通过现有模型准确，直接地捕获潜在模式。特征选择（FS）已成为识别和筛选出最相关属性的必要策略。但是，微阵列数据集的高维性对大多数现有的FS算法提出了严峻的挑战。为此，我们提出了一种新颖的特征选择策略，称为IG-MBKH。该策略集成了基于信息增益（IG）的特征分级预筛选方法和改进的二进制磷虾群（MBKH）算法。使用MBKH，双曲正切函数，自适应传递因子来搜索特征子集时，为了更好地搜索可能的特征子集，引入了混沌记忆加权因子。结果表明，与BKH，MBKH和几种最新算法相比，IG-MBKH算法可以在收敛性，特征数量和分类准确性方面实现改进。此外，我们评估了不同分类器对我们提出的策略性能的影响。