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نتیجه جستجو - Ensemble of classifiers

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 Adaptation of the idea of concept drift to some behavioral biometrics: Preliminary studies
انطباق ایده رانش مفهوم با برخی از بیومتریک های رفتاری: مطالعات اولیه-2021
In this paper we present a novel strategy that utilizes concept drift to improve some biometric procedures. The proposed method can be applied whenever behavioral signals change and those changes need to be detected. From a security point of view, this is important because detection of and appropriate response to change should result in some alteration in the operation of the biometric system. As one example, this allows for the detection of legitimate and illegitimate users. Experiments performed on real biometric signals have demonstrated that the proposed techniques could be introduced into existing professional biometric systems based on behavioral features.
Keywords: Concept drift | Biometrics | Classifiers | Ensemble of classifiers
مقاله انگلیسی
2 Texture descriptors and voxels for the early diagnosis of Alzheimer’s disease
توصیف کنندگان بافت و وکسل ها برای تشخیص زودرس بیماری آلزایمر-2019
Background and objective: Early and accurate diagnosis of Alzheimers Disease (AD) is critical since early treatment effectively slows the progression of the disease thereby adding productive years to those afflicted by this disease. A major problem encountered in the classification of MRI for the automatic diagnosis of AD is the socalled curse-of-dimensionality, which is a consequence of the high dimensionality of MRI feature vectors and the low number of training patterns available in most MRI datasets relevant to AD. Methods: A method for performing early diagnosis of AD is proposed that combines a set of SVMs trained on different texture descriptors (which reduce dimensionality) extracted from slices of Magnetic Resonance Image (MRI) with a set of SVMs trained on markers built from the voxels of MRIs. The dimension of the voxel-based features is reduced by using different feature selection algorithms, each of which trains a separate SVM. These two sets of SVMs are then combined by weighted-sum rule for a final decision. Results: Experimental results show that 2D texture descriptors improve the performance of state-of-the-art voxelbased methods. The evaluation of our system on the four ADNI datasets demonstrates the efficacy of the proposed ensemble and demonstrates a contribution to the accurate prediction of AD. Conclusions: Ensembles of texture descriptors combine partially uncorrelated information with respect to standard approaches based on voxels, feature selection, and classification by SVM. In other words, the fusion of a system based on voxels and an ensemble of texture descriptors enhances the performance of voxel-based approaches.
Keywords: Alzheimer’s disease | Ensemble of classifiers | Pattern recognition | Feature selection
مقاله انگلیسی
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