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The Parkinson's disease (PD) detection based on dysphonia has been drawn significant attention. However, all dysphonia measurements differ in the uncontrolled acoustic environments. In order to gain as much reliability as possible, measurements should be assessed and the robust ones are chosen. In this study, motivated by statistical learning theory, the problem of PD detection is addressed to classify the participant as healthy or PD using support vector machine (SVM) with the dysphonia measurements as the input feature vector. Therefore an energy-based feature-ranking algorithm is adopted to assess the dysphonia measurements. Moreover, in order to improve the stability of the proposed algorithm, an ensemble version is also presented where multiple feature-ranking results are aggregated. The experimental results on PD data sets have shown the proposed algorithm outperforms other classic ones, and the ensemble version obtain the higher stability than single one.