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.

中文翻译：

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一种新的基于最小二乘核极限学习机的递归基因选择方法

本文提出了一种递归特征消除 (RFE) 机制，通过最小二乘核极限学习机 (LSKELM) 分类器来选择信息量最大的基因。以与小权重范数相关的方式描述 LSKELM 的泛化能力，我们提出了一个排序标准，通过 LSKELM 获得的权重范数来评估基因的重要性。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 输出权重范数给出的基因重要性对基因进行排序，最后去除“最不重要”的基因。受益于极限学习机（ELM）内核的随机映射机制，LSKELM-RFE 的参数无需手动调优。