• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >An Enhanced Fuzzy Min–Max Neural Network for Pattern Classification
【24h】

An Enhanced Fuzzy Min–Max Neural Network for Pattern Classification

机译：增强的模糊最小-最大神经网络用于模式分类

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

An enhanced fuzzy min-max (EFMM) network is proposed for pattern classification in this paper. The aim is to overcome a number of limitations of the original fuzzy min-max (FMM) network and improve its classification performance. The key contributions are three heuristic rules to enhance the learning algorithm of FMM. First, a new hyperbox expansion rule to eliminate the overlapping problem during the hyperbox expansion process is suggested. Second, the existing hyperbox overlap test rule is extended to discover other possible overlapping cases. Third, a new hyperbox contraction rule to resolve possible overlapping cases is provided. Efficacy of EFMM is evaluated using benchmark data sets and a real medical diagnosis task. The results are better than those from various FMM-based models, support vector machine-based, Bayesian-based, decision tree-based, fuzzy-based, and neural-based classifiers. The empirical findings show that the newly introduced rules are able to realize EFMM as a useful model for undertaking pattern classification problems.
机译：提出了一种改进的模糊最小极大值（EFMM）网络用于模式分类。目的是克服原始模糊最小-最大（FMM）网络的许多限制，并提高其分类性能。关键贡献在于三个启发式规则，以增强FMM的学习算法。首先，提出了一种新的超级框扩展规则，以消除超级框扩展过程中的重叠问题。其次，扩展了现有的超级框重叠测试规则，以发现其他可能的重叠情况。第三，提供了新的超框收缩规则来解决可能的重叠情况。使用基准数据集和实际的医学诊断任务评估EFMM的疗效。结果优于来自各种基于FMM的模型，基于支持向量机，基于贝叶斯，基于决策树，基于模糊和基于神经的分类器的结果。实验结果表明，新引入的规则能够将EFMM实现为处理模式分类问题的有用模型。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号