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首页> 外文期刊>Knowledge-Based Systems >Combining complementary information sources in the Dempster-Shafer framework for solving classification problems with imperfect labels
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Combining complementary information sources in the Dempster-Shafer framework for solving classification problems with imperfect labels

机译：结合Dempster-Shafer框架中的补充信息源以解决具有不完善标签的分类问题

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摘要

This paper presents a novel supervised classification approach in the ensemble learning and Dempster-Shafer frameworks for handling data with imperfect labels. Through a re-labeling procedure and by utilizing the prototypes of the pre-defined classes, the possible uncertainty in the label of each learning sample is detected and based on the level of ambiguity concerning its class membership, it is assigned to only one class or a subset of the pre-defined classes. In order to properly estimate the class labels, com plementary representations of the data are employed using a diversity-based feature space selection method. Multilayer perceptrons neural network is used to learn characteristics of the data with new labels in each feature space. For a given test pattern the outputs of the neural networks, which are gen erated based on the evidences raised from the feature spaces, are considered as basic belief assignments (BBAs). The BBAs represent partial knowledge of a test sample's class and are combined using Dempster's rule of combination. Experiments on artificial and real data demonstrate that by considering the ambigu ity in labels of the data, the proposed method can provide better results than single and ensemble clas sifiers that solve the classification problem using data with initial imperfect labels.
机译：本文提出了一种集成学习和Dempster-Shafer框架中新颖的监督分类方法，用于处理标签不完善的数据。通过重新标注程序并利用预定义类的原型，可以检测每个学习样本的标签中可能存在的不确定性，并根据有关其类成员的歧义程度将其仅分配给一个类或预定义类的子集。为了适当地估计类别标签，使用基于分集的特征空间选择方法来采用数据的互补表示。多层感知器神经网络用于在每个特征空间中使用新标签来学习数据特征。对于给定的测试模式，基于从特征空间提出的证据生成的神经网络输出被视为基本信念分配（BBA）。 BBA代表对测试样品类别的部分知识，并使用Dempster的合并规则进行合并。人工数据和真实数据的实验表明，通过考虑数据标签的歧义性，该方法比使用带有初始不完美标签的数据解决分类问题的单个分类器和整体分类器可以提供更好的结果。

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  • 来源
    《Knowledge-Based Systems》 |2012年第2012期|p.92-102|共11页
  • 作者

    Mahdi Tabassian; Reza Ghaderi; Reza Ebrahimpour;

  • 作者单位

    Faculty of Electrical and Computer Engineering, Babol University of Technology, Babol, Iran,School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), P.O. Box 19395-5746, Niavaran Sq., Tehran, Iran;

    Faculty of Electrical and Computer Engineering, Babol University of Technology, Babol, Iran;

    School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), P.O. Box 19395-5746, Niavaran Sq., Tehran, Iran,Brain and Intelligent Systems Research Lab, Department of Electrical and Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran;

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  • 正文语种 eng
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  • 关键词

    data with imperfect labels; dempster-shafer theory; transferable belief model; feature space selection; classifier combination; MLP neural network;

    机译：标签数据不完善;Dempster-Shafer理论;可转移的信念模型特征空间选择;分类器组合;MLP神经网络;

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