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Belief-based system for fusing multiple classification results with local weights

机译：基于信仰的系统，融合多种分类结果与当地权重

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摘要

The fusion of multiple classifiers is an effective way to improve classification performance. Classifiers trained on different datasets generally have different qualities on classification. Moreover, the classification results of different objects (patterns) by a common classifier may also show different reliabilities. So, we propose a system for fusing multiple classification results with local weights based on belief functions theory. For one classifier, the training dataset is divided into some clusters and each cluster is used to train a weight to represent the reliability of this classifier used for classifying objects in this cluster. Thus, each classifier has multiple different weights corresponding to the patterns in different clusters. The weights can be optimized by minimizing the sum of distances between the weighted fusion results and the truths in all clusters. For each classifier, the object to classify is first assigned to the closest cluster according to its attributes, and then its classification result will be discounted with the corresponding weight. Multiple discounted results are combined using Dempster's rule. To reduce the errors, a soft decision-making rule is developed by modeling the partial imprecision. If a hard decision shows a high risk of error, this object will be committed to a set of possible classes. Such imprecision that can be clarified using other techniques is usually considered better than an error. So classification efficiency of imprecise decision is defined to be lower than that of a correct result but higher than that of an error. For the object to classify, the final decision is made via comparing the efficiency of hard decisions with that of imprecise decisions using patterns in the closest cluster. Finally, some real datasets are used in experimental applications to demonstrate the effectiveness of the proposed method by comparison with other related fusion methods. information fusion; belief functions; classification; weighted combination.
机译：多种分类器的融合是提高分类性能的有效方法。在不同数据集上培训的分类器通常对分类具有不同的品质。此外，公共分类器的不同对象（图案）的分类结果还可以示出不同的可靠性。因此，我们提出了一种基于信仰功能理论的局部权重融合多种分类结果的系统。对于一个分类器，训练数据集分为一些群集，每个群集用于训练权重以表示该分类器的可靠性用于对此集群中的对象进行分类。因此，每个分类器具有对应于不同簇中的模式的多个不同权重。通过最小化加权融合结果与所有集群中的真实性之间的距离之和，可以优化权重。对于每个分类器，首先根据其属性分配给分类的对象，然后其分类结果将折扣相应的权重。使用Dempster的规则相结合多个折扣结果。为了减少错误，通过建模部分不精确来开发软决策规则。如果硬判决显示出高风险的错误，则该对象将致力于一组可能的类。可以使用其他技术澄清的这种不精确通常被认为优于错误。因此，不精确的决定的分类效率被定义为低于正确结果的效率，但高于错误的结果。对于对象进行分类，通过将难度决策的效率与最近的集群中的模式进行比较，通过比较难度决策的效率来进行最终决定。最后，在实验应用中使用一些实际数据集来证明所提出的方法的有效性与其他相关融合方法的比较。信息融合;信念;分类;加权组合。

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  • 来源
    《Optical engineering》 |2019年第4期|041604.1-041604.9|共9页
  • 作者

    Yi-xiao Sun; Lin Song; Zhun-ga Liu;

  • 作者单位

    Northwestern Polytechnical University School of Mechanical Engineering Xi'an China;

    Xi'an University of Architecture and Technology School of Information and Control Engineering Xi'an China;

    Northwestern Polytechnical University School of Automation Xi'an China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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