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On measuring confidence levels using multiple views of feature set for useful unlabeled data selection

机译:使用功能集的多个视图测量置信度以进行有用的未标记数据选择

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摘要

This paper concerns the use of multiple views of a feature set to select a small amount of useful unlabeled data. In the semi-supervised learning (SSL) approach, using a selection strategy, strongly discriminative examples are first selected from unlabeled data and then, together with labeled data, utilized for training a (supervised) classifier or used for re-training the ensemble classifier. In this scenario, the selection strategy plays an important role in improving classification performance. This paper investigates a new selection strategy for a case in which the data are composed of different multiple views: first, multiple views of the data are derived independently; second, each of the views are used to measure corresponding confidence levels with which examples to be selected are evaluated; third, all the confidence levels measured from the multiple views are used as a weighted average to derive the target confidence; this select-and-train process is repeated for a pre-defined number of iterations. The experimental results, obtained using semi-supervised support vector machines for synthetic and real-life benchmark data, demonstrate that the proposed mechanism can compensate for the shortcomings of traditional strategies. In particular, the results demonstrate that when the data is appropriately decomposed into multiple views, this strategy can achieve further improved results in terms of the classification accuracy. (C) 2015 Elsevier B.V. All rights reserved.
机译:本文涉及使用功能集的多个视图来选择少量有用的未标记数据。在半监督学习(SSL)方法中,使用选择策略,首先从未标记的数据中选择强区分性示例,然后将其与标记的数据一起用于训练(监督的)分类器或重新训练集成分类器。在这种情况下,选择策略在提高分类性能方面起着重要作用。本文研究一种针对数据由不同的多个视图组成的情况的新选择策略:首先,数据的多个视图是独立导出的;其次,每个视图用于测量相应的置信度,以此评估要选择的示例;第三,将从多个视图测得的所有置信度用作加权平均值,以得出目标置信度;重复此选择训练过程以进行预定义的迭代次数。使用半监督支持向量机获得的合成和现实基准数据的实验结果表明,所提出的机制可以弥补传统策略的不足。尤其是,结果表明,当将数据适当地分解为多个视图时,此策略可以在分类准确性方面实现进一步改进的结果。 (C)2015 Elsevier B.V.保留所有权利。

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