Diversity measures for multiple classifier system analysis and design
Diversity measures for multiple classifier system analysis and design
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DOI：
10.1016/j.inffus.2004.04.002
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发表时间：
2004-05
期刊：
影响因子：
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通讯作者：
T. Windeatt
中科院分区：
文献类型：
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作者：
T. Windeatt
在多分类器系统的背景下，基分类器之间的多样性是提高集成性能的必要条件。本文比较了几种成对差异度量对泛化误差的预测能力。对于两类问题，还提出了一种新的成对度量，该度量是在模式对而不是分类器对之间计算的。实验表明，当基分类器的复杂度发生系统变化时，该方法与基分类器测试误差有很好的相关性。然而，与单位加权和和投票的相关性较弱，这表明选择最优融合的基分类器复杂性是困难的。还研究了一种基于加权组合的替代策略，并表明该策略对训练周期数不太敏感。
In the context of Multiple Classifier Systems, diversity among base classifiers is known to be a necessary condition for improvement in ensemble performance. In this paper the ability of several pair-wise diversity measures to predict generalisation error is compared. A new pair-wise measure, which is computed between pairs of patterns rather than pairs of classifiers, is also proposed for two-class problems. It is shown experimentally that the proposed measure is well correlated with base classifier test error as base classifier complexity is systematically varied. However, correlation with unity-weighted sum and vote is shown to be weaker, demonstrating the difficulty in choosing base classifier complexity for optimal fusion. An alternative strategy based on weighted combination is also investigated and shown to be less sensitive to number of training epochs.