Feature weight estimation for gene selection: a local hyperlinear learning approach.

ABSTRACT:

Background

Modeling high-dimensional data involving thousands of variables is particularly important for gene expression profiling experiments, nevertheless,it remains a challenging task. One of the challenges is to implement an effective method for selecting a small set of relevant genes, buried in high-dimensional irrelevant noises. RELIEF is a popular and widely used approach for feature selection owing to its low computational cost and high accuracy. However, RELIEF based methods suffer from instability, especially in the presence of noisy and/or high-dimensional outliers.

Results

We propose an innovative feature weighting algorithm, called LHR, to select informative genes from highly noisy data. LHR is based on RELIEF for feature weighting using classical margin maximiz

SUBMITTER: Cai H 

PROVIDER: S-EPMC4007530 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature