利用遗传算法改进简化模糊ARTMAP脑指纹分类性能

Ramaswamy Palaniappan, Shankar M. Krishnan, Chikkanan Eswaran
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引用次数: 4

摘要

提出了一种遗传算法对简化模糊ARTMAP (SFA)分类器训练过程中的输入模式进行排序,以提高基于脑指纹的个体识别分类性能。结果表明,与现有的模式排序方法(即投票策略和最小-最大)相比,该方法的分类性能有所提高。由于排序方法是通用的,因此在使用SFA时,它可以用于任何数据集,以获得更好的分类性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving Simplified Fuzzy ARTMAP Performance Using Genetic Algorithm for Brain Fingerprint Classification
A genetic algorithm is proposed for ordering the input patterns during training for simplified fuzzy ARTMAP (SFA) classifier to improve the individual identification classification performance using brain fingerprints. The results indicate improved classification performance as compared to the existing methods for pattern ordering, namely voting strategy and min-max. As the ordering method is general, it could be used with any dataset to obtain improved classification performance when SFA is used.
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