Bioinformatics Advance Access originally published online on January 20, 2006
Bioinformatics 2006 22(7):837-842; doi:10.1093/bioinformatics/btl008
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Genetic test bed for feature selection
1Department of Electrical and Computer Engineering, Texas A&M University College Station, TX 77843, USA
2TGen 445 North Fifth Street, Phoenix, AZ 85004, USA
*To whom correspondence should be addressed.
Motivation: Given a large set of potential features, such as the set of all gene-expression values from a microarray, it is necessary to find a small subset with which to classify. The task of finding an optimal feature set of a given size is inherently combinatoric because to assure optimality all feature sets of a given size must be checked. Thus, numerous suboptimal feature-selection algorithms have been proposed. There are strong impediments to evaluate feature-selection algorithms using real data when data are limited, a common situation in genetic classification. The difficulty is compound. First, there are no class-conditional distributions from which to draw data points, only a single small labeled sample. Second, there are no test data with which to estimate the feature-set errors, and one must depend on a training-data-based error estimator. Finally, there is no optimal feature set with which to compare the feature sets found by the algorithms.
Results: This paper describes a genetic test bed for the evaluation of feature-selection algorithms. It begins with a large biological feature-label dataset that is used as an empirical distribution and, using massively parallel computation, finds the top feature sets of various sizes based on a given sample size and classification rule. The user can draw random samples from the data, apply a proposed algorithm, and evaluate the proficiency of the proposed algorithm via three different measures (code provided). A key feature of the test bed is that, once a dataset is input, a single command creates the entire test bed relative to the dataset. The particular dataset used for the first version of the test bed comes from a microarray-based classification study that analyzes a large number of microarrays, prepared with RNA from breast tumor samples from each of 295 patients.
Availability: The software and supplementary material are available at http://public.tgen.org/tgen-cb/support/testbed/
Contact: edward{at}ece.tamu.edu
Received on September 30, 2005; revised on January 12, 2006; accepted on January 13, 2006
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  C. Sima and E. R. Dougherty What should be expected from feature selection in small-sample settings Bioinformatics, October 1, 2006; 22(19): 2430 - 2436. [Abstract] [Full Text] [PDF]
|
|