A kernel-based multivariate feature selection method for microarray data classification.

ABSTRACT: High dimensionality and small sample sizes, and their inherent risk of overfitting, pose great challenges for constructing efficient classifiers in microarray data classification. Therefore a feature selection technique should be conducted prior to data classification to enhance prediction performance. In general, filter methods can be considered as principal or auxiliary selection mechanism because of their simplicity, scalability, and low computational complexity. However, a series of trivial examples show that filter methods result in less accurate performance because they ignore the dependencies of features. Although few publications have devoted their attention to reveal the relationship of features by multivariate-based methods, these methods describe relationships among features onl

SUBMITTER: Sun S 

PROVIDER: S-EPMC4105478 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature