title: Improving feature selection performance using pairwise pre-evaluation.
abstract: BACKGROUND
  Biological data such as microarrays contain a huge number of features. Thus, it is necessary to select a small number of novel features to characterize the entire dataset. All combinations of the features subset must be evaluated to produce an ideal feature subset, but this is impossible using currently available computing power. Feature selection or feature subset selection provides a sub-optimal solution within a reasonable amount of time.
  
  
  RESULTS
  In this study, we propose an improved feature selection method that uses information based on all the pairwise evaluations for a given dataset. We modify the original feature selection algorithms to use pre-evaluation information. The pre-evaluation captures the quality and interactions between two features. The feature subset should be improved by using the top ranking pairs for two features in the selection process.
  
  
  CONCLUSIONS
  Experimental results demonstrated that the proposed method improved the quality of the feature subset produced by modified feature selection algorithms. The proposed method can be applied to microarray and other high-dimensional data.
authors.affiliation: Department of Nanobiomedical Science, Dankook University, Cheonan, 330-714, Korea.|Department of Computer Science and Technologies, Yanbian University of Science & Technology, Yanji City, China.
authors.forename: Songlu
authors.initials: S
authors.lastname: Li
authors.affiliation: Department of Nanobiomedical Science, Dankook University, Cheonan, 330-714, Korea. sejongoh@dankook.ac.kr.
authors.forename: Sejong
authors.initials: S
authors.lastname: Oh
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D015870:Gene Expression; D012372:ROC Curve; D012984:Software
keywords: Classification; Feature interaction; Feature selection; Filter method