DNA Microarray technology is an emergent field, which offers the possibility of obtaining simultaneous estimates of the expression levels of several thousand genes in an organism in a single experiment. One of the most significant challenges in this research field is to select high relevant genes from gene expression data. To address this problem, feature selection is a well-known technique to eliminate unnecessary genes in order to ensure accurate classification results. This paper proposes a binary version of Political Optimizer (PO) to solve feature selection problem using gene expression data. Two transfer functions are used to design a binary PO. The first one is based on Sigmoid function and will be noted as BPO-S, while the second one is based on V-shaped function and will be noted as BPO-V. The proposed methods are evaluated using 9 biological datasets and compared with 8 binary well-known metaheuristics. The comparative results show the prevalent performance of the BPO methods especially BPO-V in comparison with other techniques.

中文翻译：

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使用基因表达数据进行特征选择的二进制政治优化器

DNA微阵列技术是一个新兴领域，它提供了在单个实验中同时获得生物体中数千个基因表达水平的可能性。该研究领域中最重大的挑战之一是从基因表达数据中选择高度相关的基因。为了解决该问题，特征选择是消除不必要基因以确保准确分类结果的公知技术。本文提出了政治优化器（PO）的二进制版本，以解决使用基因表达数据的特征选择问题。使用两个传递函数来设计二进制PO。第一个基于Sigmoid函数，将被标记为BPO-S，而第二个基于V型函数，将被标记为BPO-V。所提出的方法使用9个生物学数据集进行了评估，并与8种二元众所周知的元启发式方法进行了比较。比较结果表明，与其他技术相比，BPO方法（尤其是BPO-V）具有普遍的性能。