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首页> 外文会议>Conference on Object Detection, Classification, and Tracking Technologies Oct 22-24, 2001, Wuhan, China >A new algorithm for combining classifiers based on fuzzy integral and genetic algorithms
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A new algorithm for combining classifiers based on fuzzy integral and genetic algorithms

机译：基于模糊积分和遗传算法的分类器组合新算法

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摘要

Combination of many different classifiers can improve classification accuracy. Sugeno and choquet integrals with respect to the fuzzy measure possess many desired properties, so in this paper they are used to combine multiple neural network classifiers. However, it is difficult to determine fuzzy measures in real problems. In this paper, we present two methods, one is that we assign the degree of importance of each network based on how good these networks classify each class of the training data, the other is by genetic algorithms (GAs), to obtain fuzzy measures, each taking into account the intuitive idea that each classifier always possesses different classification ability for each class. In the experiment, several databases in UCI repository are tested using these combination schemes and compared with C4.5. They are also applied to a multisensor fusion system for workpiece identification. Experimental results confirm the superiority of these presented methods.
机译：许多不同分类器的组合可以提高分类精度。关于模糊测度的Sugeno和Chquet积分具有许多所需的属性，因此在本文中将它们用于组合多个神经网络分类器。但是，在实际问题中很难确定模糊测度。在本文中，我们提出了两种方法，一种是根据这些网络对训练数据的各个类别进行分类的程度来分​​配每个网络的重要性，另一种是通过遗传算法（GA）获得模糊测度，每个分类器都考虑到每个分类器始终具有不同分类能力的直观思想。在实验中，使用这些组合方案对UCI存储库中的几个数据库进行了测试，并与C4.5进行了比较。它们还应用于多传感器融合系统以进行工件识别。实验结果证实了这些方法的优越性。

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