Particle swarm optimization for prototype reduction
Particle swarm optimization for prototype reduction
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DOI：
10.1016/j.neucom.2008.03.008
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发表时间：
2009
期刊：
影响因子：
6
通讯作者：
L. Nanni;A. Lumini
中科院分区：
文献类型：
--
作者：
L. Nanni;A. Lumini
本文解决的问题涉及最近邻分类器的原型简化。这里提出了一种基于粒子群优化的有效方法来寻找一组好的原型。从最初随机选择少量训练模式开始，我们使用粒子群优化生成一组原型，从而最大限度地减少训练集上的错误率。为了提高分类性能，在训练阶段重复生成原型N次，然后使用生成的N组原型中的每一个对每个测试模式进行分类，最后通过“投票规则”将这N个分类结果组合起来。通过使用多个基准数据集进行的实验验证了相对于最先进方法的性能改进。
The problem addressed in this paper concerns the prototype reduction for a nearest-neighbor classifier. An efficient method based on particle swarm optimization is proposed here for finding a good set of prototypes. Starting from an initial random selection of a small number of training patterns, we generate a set of prototypes, using the particle swarm optimization, which minimizes the error rate on the training set. To improve the classification performance, during the training phase the prototype generation is repeated N times, then each of the resulting N sets of prototypes is used to classify each test pattern, and finally these N classification results are combined by the “vote rule”. The performance improvement with respect to the state-of-the-art approaches is validated through experiments with several benchmark datasets.