Cancer is a severe condition of uncontrolled cell division that results in a tumor formation that spreads to other tissues of the body. Therefore, the development of new medication and treatment methods for this is in demand. Classification of microarray data plays a vital role in handling such situations. The relevant gene selection is an important step for the classification of microarray data. This work presents gene encoder, an unsupervised two-stage feature selection technique for the cancer samples’ classification. The first stage aggregates three filter methods, namely principal component analysis, correlation, and spectral-based feature selection techniques. Next, the genetic algorithm is used, which evaluates the chromosome utilizing the autoencoder-based clustering. The resultant feature subset is used for the classification task. Three classifiers, namely support vector machine, k-nearest neighbors, and random forest, are used in this work to avoid the dependency on any one classifier. Six benchmark gene expression datasets are used for the performance evaluation, and a comparison is made with four state-of-the-art related algorithms. Three sets of experiments are carried out to evaluate the proposed method. These experiments are for the evaluation of the selected features based on sample-based clustering, adjusting optimal parameters, and for selecting better performing classifier. The comparison is based on accuracy, recall, false positive rate, precision, F-measure, and entropy. The obtained results suggest better performance of the current proposal.

癌症是细胞分裂不受控制的严重疾病，会导致肿瘤形成并扩散到身体的其他组织。因此，需要开发新的药物和治疗方法。微阵列数据的分类在处理此类情况中起着至关重要的作用。相关的基因选择是微阵列数据分类的重要步骤。这项工作提出了基因编码器，一种用于癌症样本分类的无监督两阶段特征选择技术。第一阶段汇总了三种滤波方法，即主成分分析，相关性和基于光谱的特征选择技术。接下来，使用遗传算法，该算法利用基于自动编码器的聚类评估染色体。生成的特征子集用于分类任务。在这项工作中，使用k近邻和随机森林来避免依赖于任何一个分类器。六个基准基因表达数据集用于性能评估，并与四种最新的相关算法进行了比较。进行了三组实验以评估该方法。这些实验用于基于样本的聚类评估所选特征，调整最佳参数以及选择性能更好的分类器。比较是基于准确性，召回率，误报率，准确性，F测度和熵。获得的结果表明当前建议的性能更好。