Parikh Devi, Polikar Robi
Electrical and Computer Engineering, Rowan University, Glassboro, NJ 08028, USA.
IEEE Trans Syst Man Cybern B Cybern. 2007 Apr;37(2):437-50. doi: 10.1109/tsmcb.2006.883873.
This paper introduces Learn++, an ensemble of classifiers based algorithm originally developed for incremental learning, and now adapted for information/data fusion applications. Recognizing the conceptual similarity between incremental learning and data fusion, Learn++ follows an alternative approach to data fusion, i.e., sequentially generating an ensemble of classifiers that specifically seek the most discriminating information from each data set. It was observed that Learn++ based data fusion consistently outperforms a similarly configured ensemble classifier trained on any of the individual data sources across several applications. Furthermore, even if the classifiers trained on individual data sources are fine tuned for the given problem, Learn++ can still achieve a statistically significant improvement by combining them, if the additional data sets carry complementary information. The algorithm can also identify-albeit indirectly-those data sets that do not carry such additional information. Finally, it was shown that the algorithm can consecutively learn both the supplementary novel information coming from additional data of the same source, and the complementary information coming from new data sources without requiring access to any of the previously seen data.
本文介绍了Learn++，这是一种基于分类器集成的算法，最初是为增量学习而开发的，现在适用于信息/数据融合应用。认识到增量学习和数据融合之间的概念相似性，Learn++采用了一种替代的数据融合方法，即依次生成一个分类器集成，专门从每个数据集中寻找最具区分性的信息。据观察，基于Learn++的数据融合在多个应用中始终优于在任何单个数据源上训练的类似配置的集成分类器。此外，即使在单个数据源上训练的分类器针对给定问题进行了微调，如果额外的数据集携带互补信息，Learn++通过组合它们仍然可以实现统计学上的显著改进。该算法还可以间接识别那些不携带此类额外信息的数据集。最后，结果表明该算法可以连续学习来自同一数据源的额外数据的补充新信息，以及来自新数据源的互补信息，而无需访问任何先前见过的数据。
