Gupta Lalit, Kota Srinivas, Molfese Dennis L, Vaidyanathan Ravi
Department of Electrical and Computer Engineering, Southern Illinois University, Carbondale, IL 62901, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:6304-7. doi: 10.1109/IEMBS.2010.5628090.
Fusion classifiers with diverse components (classifiers or data sets) outperform those with less diverse components. Determining component diversity, therefore, is of the utmost importance in the design of fusion classifiers which are often employed in clinical diagnostic and numerous other pattern recognition problems. In this paper, a new pairwise diversity-based ranking strategy is introduced to select a subset of ensemble components, which when combined, will be more diverse than any other component subset of the same size. The strategy is unified in the sense that the components can be either polychotomous classifiers or polychotomous data sets. Classifier fusion and data fusion systems are formulated based on the diversity selection strategy and the application of the two fusion strategies are demonstrated through the classification of multi-channel event related potentials (ERPs). From the results it is concluded that data fusion outperforms classifier fusion. It is also shown that the diversity-based data fusion system outperforms the system using randomly selected data components. Furthermore, it is demonstrated that the combination of data components that yield the best performance, in a relative sense, can be determined through the diversity selection strategy.
具有不同组件（分类器或数据集）的融合分类器比具有较少不同组件的融合分类器表现更好。因此，确定组件的多样性在融合分类器的设计中至关重要，融合分类器常用于临床诊断和许多其他模式识别问题。本文引入了一种基于成对多样性的新排序策略来选择集成组件的一个子集，当这些组件组合在一起时，将比任何其他相同大小的组件子集更加多样化。该策略是统一的，因为组件可以是多分类器或多分类数据集。基于多样性选择策略制定了分类器融合和数据融合系统，并通过多通道事件相关电位（ERP）的分类展示了这两种融合策略的应用。从结果可以得出结论，数据融合优于分类器融合。还表明，基于多样性的数据融合系统优于使用随机选择的数据组件的系统。此外，证明了在相对意义上，可以通过多样性选择策略确定产生最佳性能的数据组件组合。
