A Novel Recursive Gene Selection Method Based on Least Square Kernel Extreme Learning Machine

doi:10.1109/TCBB.2021.3068846

A Novel Recursive Gene Selection Method Based on Least Square Kernel Extreme Learning Machine

2022-07-01

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DOI：10.1109/TCBB.2021.3068846

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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.

Keywords:

Kernel

Support vector machines

Classification algorithms

Filtering theory

Tumors

Time complexity

Testing

The norm of the output weights

gene selection

least square

kernel extreme learning machine

recursive feature elimination

A Novel Recursive Gene Selection Method Based on Least Square Kernel Extreme Learning Machine

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