A divide-and-conquer method for multi-net classifiers
A divide-and-conquer method for multi-net classifiers
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DOI：
10.1007/s10044-002-0174-6
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发表时间：
2003-04-01
影响因子：
3.9
通讯作者：
Likas, A
中科院分区：
文献类型：
--
作者：
Frosyniotis, D;Stafylopatis, A;Likas, A
一些研究人员已经证明，通过组合多个神经网络的输出，可以在困难的模式识别问题中实现实质性的改进。在这项工作中，我们提出并测试了一个基于监督和无监督学习的模式分类多网络系统。在“分治”框架之后，输入空间被划分为重叠的子空间，随后使用神经网络来解决相应的分类子任务。最后，将各个分类器的输出进行适当的组合，以获得最终的分类决策。两种聚类方法已被应用于输入空间划分和两个计划已被认为是多个分类器的输出相结合。在知名数据集上的实验表明，与单网络训练的情况相比，多网络分类系统在错误率和训练速度方面（特别是如果分类器的训练是并行进行的）都表现出良好的性能。
Several researchers have shown that substantial improvements can be achieved in difficult pattern recognition problems by combining the outputs of multiple neural networks. In this work, we present and test a pattern classification multi-net system based on both supervised and unsupervised learning. Following the 'divide-and-conquer' framework, the input space is partitioned into overlapping subspaces and neural networks are subsequently used to solve the respective classification subtasks. Finally, the outputs of individual classifiers are appropriately combined to obtain the final classification decision. Two clustering methods have been applied for input space partitioning and two schemes have been considered for combining the outputs of the multiple classifiers. Experiments on well-known data sets indicate that the multi-net classification system exhibits promising performance compared with the case of single network training, both in terms of error rates and in terms of training speed (especially if the training of the classifiers is done in parallel).