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سند علمی

A Feature Selection Method Based on a Support Vector Machine and the Cumulative Distribution Function

Jen-Ing G. Hwang, Chih-En Liu

چکیده

Feature selection is an important issue in the research areas of machine learning and data mining. It reduces the dimensionality of data and enhances the performance of data analysis and interpretability, such as clustering or classification algorithms. This paper proposes a feature selection method based on support vector machines and distance-based cumulative distribution functions. This method closely relates to the recursive support vector machine (R-SVM) and extends from linear to nonlinear kernels. The proposed method was shown to compete well against R-SVM on both publicly available datasets and high-dimensional proteomic data.

اسناد مشابه

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