Batallones Amy, Sanchez Kilby, Mott Brian, Coffran Cameron, Frank Hsu D
Laboratory of Informatics and Data Mining, Department of Computer and Information Science, Fordham University, New York, NY, USA.
Program for the Human Environment, The Rockefeller University, New York, NY, USA.
Brain Inform. 2015 Mar;2(1):21-32. doi: 10.1007/s40708-015-0008-0. Epub 2015 Feb 3.
When combining decisions made by two separate visual cognition systems, statistical means such as simple average (M ) and weighted average (M and M ), incorporating the confidence level of each of these systems have been used. Although combination using these means can improve each of the individual systems, it is not known when and why this can happen. By extending a visual cognition system to become a scoring system based on each of the statistical means M , M , and M respectively, the problem of combining visual cognition systems is transformed to the problem of combining multiple scoring systems. In this paper, we examine the combined results in terms of performance and diversity using combinatorial fusion, and study the issue of when and why a combined system can be better than individual systems. A data set from an experiment with twelve trials is analyzed. The findings demonstrated that combination of two visual cognition systems, based on weighted means M or M , can improve each of the individual systems only when both of them have relatively good performance and they are diverse.
在合并由两个独立视觉认知系统做出的决策时，已采用诸如简单平均（M）和加权平均（M 和 M）等统计方法，并纳入了每个系统的置信水平。尽管使用这些方法进行合并可以改进各个单独的系统，但尚不清楚何时以及为何会出现这种情况。通过将视觉认知系统分别扩展为基于统计方法 M、M 和 M 的评分系统，合并视觉认知系统的问题就转化为合并多个评分系统的问题。在本文中，我们使用组合融合从性能和多样性方面研究合并结果，并研究合并系统何时以及为何能够优于单个系统的问题。我们分析了来自一项包含十二次试验的实验数据集。研究结果表明，只有当两个视觉认知系统都具有相对良好的性能且它们具有多样性时，基于加权平均 M 或 M 对这两个视觉认知系统进行合并才能改进各个单独的系统。
