Application of an efficient Bayesian discretization method to biomedical data.

ABSTRACT:

Background

Several data mining methods require data that are discrete, and other methods often perform better with discrete data. We introduce an efficient Bayesian discretization (EBD) method for optimal discretization of variables that runs efficiently on high-dimensional biomedical datasets. The EBD method consists of two components, namely, a Bayesian score to evaluate discretizations and a dynamic programming search procedure to efficiently search the space of possible discretizations. We compared the performance of EBD to Fayyad and Irani's (FI) discretization method, which is commonly used for discretization.

Results

On 24 biomedical datasets obtained from high-throughput transcriptomic and proteomic studies, the classification performances of the C4.5 classifier and

SUBMITTER: Lustgarten JL 

PROVIDER: S-EPMC3162539 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature