Feature selection for high dimensional microarray gene expression data via weighted signal to noise ratio.

ABSTRACT: Feature selection in high dimensional gene expression datasets not only reduces the dimension of the data, but also the execution time and computational cost of the underlying classifier. The current study introduces a novel feature selection method called weighted signal to noise ratio (WSNR) by exploiting the weights of features based on support vectors and signal to noise ratio, with an objective to identify the most informative genes in high dimensional classification problems. The combination of two state-of-the-art procedures enables the extration of the most informative genes. The corresponding weights of these procedures are then multiplied and arranged in decreasing order. Larger weight of a feature indicates its discriminatory power in classifying the tissue samples to their true

SUBMITTER: Hamraz M 

PROVIDER: S-EPMC10128961 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature