Selective voting in convex-hull ensembles improves classification accuracy.

ABSTRACT:

Objective

Classification algorithms can be used to predict risks and responses of patients based on genomic and other high-dimensional data. While there is optimism for using these algorithms to improve the treatment of diseases, they have yet to demonstrate sufficient predictive ability for routine clinical practice. They generally classify all patients according to the same criteria, under an implicit assumption of population homogeneity. The objective here is to allow for population heterogeneity, possibly unrecognized, in order to increase classification accuracy and further the goal of tailoring therapies on an individualized basis.

Methods and materials

A new selective-voting algorithm is developed in the context of a classifier ensemble of two-dimensional convex hulls

SUBMITTER: Kodell RL 

PROVIDER: S-EPMC3666100 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature