CBFS: high performance feature selection algorithm based on feature clearness.
BACKGROUND
The goal of feature selection is to select useful features and simultaneously exclude garbage features from a given dataset for classification purposes. This is expected to bring reduction of processing time and improvement of classification accuracy.


METHODS
In this study, we devised a new feature selection algorithm (CBFS) based on clearness of features. Feature clearness expresses separability among classes in a feature. Highly clear features contribute towards obtaining high classification accuracy. CScore is a measure to score clearness of each feature and is based on clustered samples to centroid of classes in a feature. We also suggest combining CBFS and other algorithms to improve classification accuracy.


CONCLUSIONS
From the experiment we confirm that CBFS is more excellent than up-to-date feature selection algorithms including FeaLect. CBFS can be applied to microarray gene selection, text categorization, and image classification.
Department of Nanobiomedical Science and WCU Research Center of Nanobiomedical Science, Dankook University, Cheonan, South Korea.
Minseok
M
Seo
Sejong
S
Oh
D000465:Algorithms; D019540:Area Under Curve; D002965:Classification / Q000379:methods; D006801:Humans; D046228:Microarray Analysis; D009369:Neoplasms / Q000235:genetics; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D012372:ROC Curve; D060388:Support Vector Machine