Support vector regression (SVR) is a powerful kernel-based method which has been successfully applied in problems. Regarding the feature-weighted SVR algorithms, its contribution to model output taken into account. However, performance of subject feature weights and time consumption on training. In paper, an efficient proposed. Firstly, value constraint each weight obtained according maximal in...

Recently, besides the performance, the stability (robustness, i.e., the variation in feature selection results due to small changes in the data set) of feature selection is received more attention. Ensemble feature selection where multiple feature selection outputs are combined to yield more robust results without sacrificing the performance is an effective method for stable feature selection. ...

Recently, besides the performance, stability (robustness, i.e., variation in feature selection results due to small changes data set) of is received more attention. Ensemble where multiple outputs are combined yield robust without sacrificing performance an effective method for stable selection. In order make further improvements (classification accuracy), diversity regularized ensemble weighti...

Abstract.

Selecting a set of features which is optimal for a given classiication task is one of the central problems in machine learning. We address the problem using the exible and robust lter technique EUBAFES. EUBAFES is based on a feature weighting approach which computes binary feature weights and therefore a solution in the feature selection sense and also gives detailed information about feature r...