基于Choquet积分的多分类器组合样本约简

Junfen Chen, Huili Pei, Yan Li
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引用次数: 0

摘要

当我们考虑分类器之间的相互作用时,关于非加性集合函数μ的Choquet积分是一个有用的组合工具。这种组合方法以牺牲运行时间和内存空间为代价,工作得非常好。引入样本约简技术,降低了遗传算法确定非加性集函数μ的复杂度。训练集的样本约简是指每个分类器的输出约简,而不是样本本身。仿真实验表明,样本约简技术可以缩短确定非加性集函数μ的运行时间,同时大大提高了多分类器组合系统的泛化能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Choquet integral based samples reduction in multiple classifiers combination
Choquet integral with regards to a non-additive set function μ is a useful combination tool when we consider the interactions between classifiers. This combination method works very well at the expense of run time and the memory space. This paper introduces samples reduction technology to degrade the complexity of determining the non-additive set functionsμ which is determined by genetic algorithm. Reducing samples in training set refers to reduction the outputs of every classifier not the samples themselves. The simulated experiments illustrate that the samples reduction technology can low run time of determining the non-additive set function μ, at the same time, the generalization ability of multiple classifiers combination system is improved mostly.
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