Microarray gene expression data are often accompanied by a large number of genes and a small number of samples. However, only a few of these genes are relevant to cancer, resulting in signigicant gene selection challenges. Hence, we propose a two-stage gene selection approach by combining extreme gradient boosting (XGBoost) and a multi-objective optimization genetic algorithm (XGBoost-MOGA) for cancer classification in microarray datasets. In the first stage, the genes are ranked use an ensemble-based feature selection using XGBoost. This stage can effectively remove irrelevant genes and yield a group comprising the most relevant genes related to the class. In the second stage, XGBoost-MOGA searches for an optimal gene subset based on the most relevant genes's group using a multi-objective optimization genetic algorithm. We performed comprehensive experiments to compare XGBoost-MOGA with other state-of-the-art feature selection methods using two well-known learning classifiers on 13 publicly available microarray expression datasets. The experimental results show that XGBoost-MOGA yields significantly better results than previous state-of-the-art algorithms in terms of various evaluation criteria, such as accuracy, F-score, precision, and recall.

中文翻译：

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使用 XGBoost 和多目标遗传算法进行癌症分类的混合基因选择方法

微阵列基因表达数据往往伴随着大量基因和少量样本。然而，这些基因中只有少数与癌症相关，这导致了重大的基因选择挑战。因此，我们通过结合极端梯度增强（XGBoost）和多目标优化遗传算法（XGBoost-MOGA）提出了一种两阶段基因选择方法，用于微阵列数据集中的癌症分类。在第一阶段，使用 XGBoost 使用基于集成的特征选择对基因进行排序。这个阶段可以有效地去除不相关的基因，并产生一个包含与类相关的最相关基因的组。在第二阶段，XGBoost-MOGA 使用多目标优化遗传算法基于最相关的基因组搜索最佳基因子集。我们在 13 个公开可用的微阵列表达数据集上使用两个众所周知的学习分类器进行了全面的实验，以将 XGBoost-MOGA 与其他最先进的特征选择方法进行比较。实验结果表明，XGBoost-MOGA 在各种评估标准（如准确率、F-score、精度和召回率）方面的结果明显优于以前的最先进算法。