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首页> 外文会议>Conference on Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2003 Apr 23-25, 2003 Orlando, Florida, USA >Sensitivity of fusion performance to classifier model variations
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Sensitivity of fusion performance to classifier model variations

机译：融合性能对分类器模型变化的敏感性

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摘要

During design of classifier fusion tools, it is important to evaluate the performance of the fuser. In many cases, the output of the classifiers needs to be simulated to provide the range of fusion input that allows an evaluation throughout the design space. One fundamental question is how the output should be distributed, in particular for multi-class continuous output classifiers. Using the wrong distribution may lead to fusion tools that are either overly optimistic or otherwise distort the outcome. Either case may lead to a fuser that performs sub-optimal in practice. It is therefore imperative to establish the bounds of different classifier output distributions. In addition, one must take into account the design space that may be of considerable complexity. Exhaustively simulating the entire design space may be a lengthy undertaking. Therefore, the simulation has to be guided to populate the relevant areas of the design space. Finally, it is crucial to quantify the performance throughout the design of the fuser. This paper addresses these issues by introducing a simulator that allows the evaluation of different classifier distributions in combination with a design of experiment setup, and a built-in performance evaluation. We show results from an application of diagnostic decision fusion on aircraft engines.
机译：在设计分类器融合工具期间，评估定影器的性能很重要。在许多情况下，需要对分类器的输出进行仿真，以提供融合输入的范围，从而可以对整个设计空间进行评估。一个基本问题是输出应该如何分配，尤其是对于多类连续输出分类器而言。使用错误的分布可能会导致融合工具过于乐观，或者导致结果失真。无论哪种情况都可能导致定影器在实践中表现不佳。因此，必须建立不同分类器输出分布的界限。另外，必须考虑可能相当复杂的设计空间。详尽地模拟整个设计空间可能是一项漫长的工作。因此，必须指导仿真以填充设计空间的相关区域。最后，至关重要的是在整个热凝器设计中量化性能。本文通过介绍一种模拟器来解决这些问题，该模拟器允许结合实验设置的设计和内置的性能评估来评估不同的分类器分布。我们展示了诊断决策融合在飞机发动机上的应用结果。

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