Feature selection is an essential task to predict clinical risk and biomarkers from the gene expression data. For practical matters, to choose the significant genes, researchers have been addressed several classical feature selection problems over the past decades for subsequent classification of genomics datasets with large ambient dimensionality but a small number of observations. To overcome high dimensionality and overfitting issues, in this paper, we developed a new gene selection technique by combination of minimum redundancy maximum relevance (mRMR) and teaching learning-based optimization for accurate cancer prediction. Firstly, in the proposed approach, mRMR is applied to find the most discriminative genes from the original feature sets, and then a precise teaching learning-based optimization with opposition-based learning approach further refines the reduced feature set that can contribute to identifying the type of cancers. In addition, a new activation function is also investigated for effective gene selection, which is applied to convert continuous to binary search space. Support vector machine (SVM) is used as a fitness function in the proposed method to select relevant features that can help to estimate the predictive accuracy and classify cancer accurately. Attempts have made to increase the performance of SVM classifier by tuning penalty factor, kernel parameter, and tube size parameter with the help of proposed method. In order to testify computational efficiency of proposed algorithm, we have collected six gene expression datasets. Experimental results demonstrated that proposed method by utilizing SVM with Radial Basis Function kernel function is able to significantly reduce the irrelevant genes and outperform the conventional wrapper methods in terms of accuracy and model interpretation.

中文翻译：

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受人类智能启发的特征选择可提高癌症类型的分类准确性

特征选择是从基因表达数据中预测临床风险和生物标志物的一项基本任务。对于实际问题，为了选择重要的基因，研究人员在过去几十年中已经解决了几个经典的特征选择问题，以便随后对具有大环境维度但观察次数少的基因组数据集进行分类。为了克服高维和过拟合问题，在本文中，我们开发了一种新的基因选择技术，通过结合最小冗余最大相关性 (mRMR) 和基于学习的优化教学来准确预测癌症。首先，在所提出的方法中，mRMR 用于从原始特征集中找到最具辨别力的基因，然后使用基于对立的学习方法进行精确的基于教学的基于学习的优化，进一步细化了有助于识别癌症类型的简化特征集。此外，还研究了一种用于有效基因选择的新激活函数，该函数用于将连续搜索空间转换为二分搜索空间。支持向量机 (SVM) 在所提出的方法中用作适应度函数，以选择有助于估计预测准确性和准确分类癌症的相关特征。在所提出的方法的帮助下，尝试通过调整惩罚因子、核参数和管尺寸参数来提高 SVM 分类器的性能。为了验证所提出算法的计算效率，我们收集了六个基因表达数据集。