With the advancement of microarray technology, gene expression profiling has shown remarkable effort to predict the different types of malignancy and their subtypes. In microarrays, predicting highly discriminative genes is a challenging task and existing hybrid methods fail to deal with efficiently. To mitigate the curse of dimensionality problem and to improve the interpretability of discriminative genes, in this study, we developed a new hybrid wrapper approach which integrates the characteristics of teaching learning-based algorithm (TLBO) and gravitational search algorithm (GSA), called TLBOGSA. A new encoding strategy is also integrated into TLBOGSA to transmute the continuous search space to binary search space and form binary TLBOGSA. In the proposed method, firstly, minimum redundancy maximum relevance (mRMR) feature selection is employed to select relevant genes from the gene expression datasets. Then, wrapper method is applied to select the informative genes from the reduced data produced by mRMR. To improve the search capability during the evolution process, we have incorporated the gravitational search mechanism in the teaching phase. The proposed method uses naive bayes classifier as a fitness function to select the extremely judicious genes which can help to classify cancer accurately. The efficiency of proposed method is tested on ten biological datasets and compared with state-of-art computational intelligence approaches for tumor prediction. Experimental results and statistical analysis demonstrate that proposed method is significantly outperforms existing metaheuristic approaches regarding convergence rate, classification accuracy and optimal number of feature sets. The proposed method reaches above 98% classification accuracy in six datasets and maximum accuracy is achieved as 99.62% in DLBCL dataset.

随着微阵列技术的发展，基因表达谱分析已显示出巨大的努力来预测不同类型的恶性肿瘤及其亚型。在微阵列中，预测具有高度区分性的基因是一项艰巨的任务，而现有的杂交方法无法有效应对。为了减轻维度问题的诅咒并提高判别基因的可解释性，在本研究中，我们开发了一种新的混合包装方法，该方法结合了基于教学学习的算法（TLBO）和重力搜索算法（GSA）的特性，称为TLBOGSA 。TLBOGSA还集成了新的编码策略，以将连续搜索空间转换为二进制搜索空间并形成二进制TLBOGSA。在提出的方法中，首先，最小冗余最大相关性（mRMR）特征选择用于从基因表达数据集中选择相关基因。然后，采用包装方法从mRMR产生的简化数据中选择信息基因。为了提高进化过程中的搜索能力，我们在教学阶段引入了重力搜索机制。所提出的方法使用朴素贝叶斯分类器作为适应度函数来选择非常明智的基因，可以帮助准确地分类癌症。该方法的效率在十个生物学数据集上进行了测试，并与最新的计算机智能方法进行了肿瘤预测。实验结果和统计分析表明，该方法在收敛速度，分类精度和最佳特征集数量方面明显优于现有的元启发式方法。该方法在六个数据集中的分类准确率均达到98％以上，在DLBCL数据集中的最大准确率达到99.62％。