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...

Support Vector Machines are models widely used in supervised classification. The classical model minimizes a compromise between the structural risk and empirical risk. In this paper, we consider Machine with feature selection design implement bi-objective evolutionary algorithm for approximating Pareto optimal frontier of two objectives. metaheuristic is based on non-dominated sorting genetic i...

The development of sparse techniques presents a major challenge to complex nonlinear high-dimensional data. In this paper, we propose novel feature selection method for support vector regression, called FS-NSVR, which first attempts solve the problem in regression technology field. FS-NSVR preserves representative features selected system due its use matrix original space. is challenging mixed-...

Different approaches have been proposed for feature selection to obtain suitable features subset among all features. These methods search feature space for feature subsets which satisfies some criteria or optimizes several objective functions. The objective functions are divided into two main groups: filter and wrapper methods.  In filter methods, features subsets are selected due to some measu...