Traditional classifiers often fail to produce desired classification accuracy because of inadequate training samples present in microRNA (miRNA) gene expression cancer datasets. In this context, we propose a novel semi-supervised ensemble learning (SSEL) strategy combining the (advantages of) semi-supervised learning and ensemble learning which is able to produce better results than the individual constituent classifiers. The proposed method is validated using eight publicly available miRNA gene expression datasets of pancreatic and colorectal cancers with respect to classification accuracy, precision, recall, macro \(F_{1}\)-measure and kappa in comparison to six other state-of-the-art methods. The experimental results reveal that the proposed SSEL method significantly dominates other compared methods for cancer sample classification. The results of the statistical significance tests, receiver operating characteristic curve and area under curve justify the relevance of the better results in favor of the proposed method.

由于microRNA（miRNA）基因表达癌症数据集中存在的训练样本不足，传统的分类器通常无法产生所需的分类精度。在这种情况下，我们提出了一种新颖的半监督集成学习（SSEL）策略，该策略结合了半监督学习和集成学习的（优点），它能够产生比单个构成分类器更好的结果。关于分类和准确性，精确度，召回率，宏\（F_ {1} \），使用八个可公开获得的胰腺癌和大肠癌的miRNA基因表达数据集验证了该方法的有效性。-测量和kappa与其他六种最新方法相比。实验结果表明，所提出的SSEL方法在癌症样品分类的其他比较方法中明显占优势。统计显着性检验，接收器工作特性曲线和曲线下面积的结果证明了较好结果的相关性，从而有利于所提出的方法。