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An ensemble learning approach to lip-based biometric verification, with a dynamic selection of classifiers

机译:一种基于唇的生物特征验证的集成学习方法,具有动态选择的分类器

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摘要

Machine learning approaches are largely focused on pattern or object classification, where a combination of several classifier systems can be integrated to help generate an optimal or suboptimal classification decision. Multiple classification systems have been extensively developed because a committee of classifiers, also known as an ensemble, can outperform the ensemble's individual members. In this paper, a classification method based on an ensemble of binary classifiers is proposed. Our strategy consists of two phases: (1) the competence of the base heterogeneous classifiers in a pool is determined, and (2) an ensemble is formed by combining those base classifiers with the greatest competences for the given input data.We have shown that the competence of the base classifiers can be successfully calculated even if the number of their learning examples was limited. Such a situation is particularly observed with biometric data. In this paper, we propose a new biometric data structure, the Sim coefficients, along with an efficient data processing technique involving a pool of competent classifiers chosen by dynamic selection. (C) 2018 Elsevier Ltd. All rights reserved.
机译:机器学习方法主要集中于模式或对象分类,其中可以集成多个分类器系统的组合以帮助生成最佳或次优的分类决策。由于分类委员会(也称为集合)可以胜过集合的单个成员,因此已广泛开发了多个分类系统。本文提出了一种基于二元分类器集合的分类方法。我们的策略包括两个阶段:(1)确定池中基本异构分类器的能力,(2)通过将这些基本分类器与给定输入数据的最大能力相结合来形成整体。即使学习实例数量有限,基本分类器的能力也可以成功计算出来。利用生物特征数据尤其可以观察到这种情况。在本文中,我们提出了一种新的生物识别数据结构,即Sim系数,以及一种有效的数据处理技术,该技术涉及通过动态选择选择的一组合格分类器。 (C)2018 Elsevier Ltd.保留所有权利。

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