IEEE/ACM Trans Comput Biol Bioinform. 2022 Jul-Aug;19(4):2026-2038. doi: 10.1109/TCBB.2021.3068846. Epub 2022 Aug 8.
This paper presents a recursive feature elimination (RFE) mechanism to select the most informative genes with a least square kernel extreme learning machine (LSKELM) classifier. Describing the generalization ability of LSKELM in a way that is related to small norm of weights, we propose a ranking criterion to evaluate the importance of genes by the norm of weights obtained by LSKELM. The proposed method is called LSKELM-RFE which first employs the original genes to build a LSKELM classifier, and then ranks the genes according to their importance given by the norm of output weights of LSKELM and finally removes a "least important" gene. Benefiting from the random mapping mechanism of the extreme learning machine (ELM) kernel, there are no parameter of LSKELM-RFE needs to be manually tuned. A comparative study among our proposed algorithm and other two famous RFE algorithms has shown that LSKELM-RFE outperforms other RFE algorithms in both the computational cost and generalization ability.
本文提出了一种递归特征消除（RFE）机制，用于选择信息量最大的基因，使用最小二乘核极端学习机（LSKELM）分类器。通过与权重小范数相关的方式描述 LSKELM 的泛化能力，我们提出了一种基于 LSKELM 得到的权重范数来评估基因重要性的排序准则。所提出的方法称为 LSKELM-RFE，它首先使用原始基因构建 LSKELM 分类器，然后根据 LSKELM 的输出权重范数赋予的重要性对基因进行排序，最后删除一个“最不重要”的基因。受益于极端学习机（ELM）核的随机映射机制，LSKELM-RFE 不需要手动调整任何参数。我们的算法与另外两种著名的 RFE 算法的比较研究表明，LSKELM-RFE 在计算成本和泛化能力方面均优于其他 RFE 算法。
