In this paper, a robust weighted score for unbalanced data (ROWSU) is proposed for selecting the most discriminative feature for high dimensional gene expression binary classification with class-imbalance problem. The method addresses one of the most challenging problems of highly skewed class distributions in gene expression datasets that adversely affect the performance of classification algorithms. First, the training dataset is balanced by synthetically generating data points from minority class observations. Second, a minimum subset of genes is selected using a greedy search approach. Third, a novel weighted robust score, where the weights are computed by support vectors, is introduced to obtain a refined set of genes. The highest-scoring genes based on this approach are combined with the minimum subset of genes selected by the greedy search approach to form the final set of genes. The novel method ensures the selection of the most discriminative genes, even in the presence of skewed class distribution, thus improving the performance of the classifiers. The performance of the proposed ROWSU method is evaluated on $6$ gene expression datasets. Classification accuracy and sensitivity are used as performance metrics to compare the proposed ROWSU algorithm with several other state-of-the-art methods. Boxplots and stability plots are also constructed for a better understanding of the results. The results show that the proposed method outperforms the existing feature selection procedures based on classification performance from k nearest neighbours (kNN) and random forest (RF) classifiers.

翻译：针对高维基因表达二分类中存在的类别不平衡问题，本文提出了一种适用于不平衡数据的鲁棒加权评分方法（ROWSU），用于选取最具判别力的特征。该方法致力于解决基因表达数据集中高度偏斜的类别分布这一最具挑战性的问题——该问题会严重削弱分类算法的性能。首先，通过从少数类观测中合成生成数据点来平衡训练数据集。其次，采用贪心搜索方法选取最小基因子集。第三，引入基于支持向量计算权重的创新鲁棒加权评分，从而获得精炼的基因集。基于该方法得分最高的基因与贪心搜索选取的最小基因子集进行组合，形成最终基因集。该创新方法即使在类别分布偏斜的情况下也能确保选取最具判别力的基因，从而提升分类器性能。在6个基因表达数据集上对所提出的ROWSU方法进行了性能评估。采用分类准确率和灵敏度作为性能指标，将所提ROWSU算法与多种前沿方法进行对比。同时构建箱线图和稳定性图以更直观地展示结果。实验结果表明，基于k近邻（kNN）和随机森林（RF）分类器的分类性能评估显示，所提方法优于现有特征选择算法。