Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Feature selection for classification in high-dimensional spaces can improve generalization, reduce classifier complexity, and identify important, discriminating feature ?markers.? For support vector machine (SVM) classification, a widely used technique is recursive feature elimination (RFE).  demonstrate that RFE is not consistent with margin maximization, central to the SVM learning approach.  thus propose  explicit  margin-based feature elimination (MFE) for SVMs and demonstrate both improved margin and improved generalization, compared with RFE. Moreover, for the case of a nonlinear kernel,  show that RFE assumes that the squared ight vector 2-norm is strictly decreasing as features are eliminated.  demonstrate this is not true for the Gaussian kernel and, consequently, RFE may give poor results in this case. MFE for nonlinear kernels gives better margin and generalization.  also present an extension which achieves further margin gains, by optimizing only  two  degrees of freedom-the hyperplane's intercept and its squared 2-norm-with the ight vector orientation fixed.  finally introduce an extension that allows margin slackness.  compare against several alternatives, including RFE and a linear programming method that embeds feature selection within the classifier design. On high-dimensional gene microarray data sets, University of California at Irvine (UCI) repository data sets, and Alzheimer's disease brain image data, MFE methods give promising results.
['Yaman Aksu', 'David J. Miller', 'George Kesidis', 'Qing X. Yang']
I am writing a research paper on a idea Margin-Maximizing Feature Elimination Methods for Linear and Nonlinear Kernel-Based Discriminant Functions
[{"from": "human", "value": "I am writing a research paper on a idea Margin-Maximizing Feature Elimination Methods for Linear and Nonlinear Kernel-Based Discriminant Functions"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Feature selection for classification in high-dimensional spaces can improve generalization, reduce classifier complexity, and identify important, discriminating feature ?markers.? For support vector machine (SVM) classification, a widely used technique is recursive feature elimination (RFE).  demonstrate that RFE is not consistent with margin maximization, central to the SVM learning approach.  thus propose  explicit  margin-based feature elimination (MFE) for SVMs and demonstrate both improved margin and improved generalization, compared with RFE. Moreover, for the case of a nonlinear kernel,  show that RFE assumes that the squared ight vector 2-norm is strictly decreasing as features are eliminated.  demonstrate this is not true for the Gaussian kernel and, consequently, RFE may give poor results in this case. MFE for nonlinear kernels gives better margin and generalization.  also present an extension which achieves further margin gains, by optimizing only  two  degrees of freedom-the hyperplane's intercept and its squared 2-norm-with the ight vector orientation fixed.  finally introduce an extension that allows margin slackness.  compare against several alternatives, including RFE and a linear programming method that embeds feature selection within the classifier design. On high-dimensional gene microarray data sets, University of California at Irvine (UCI) repository data sets, and Alzheimer's disease brain image data, MFE methods give promising results."}]