一种用于处理具有缺失特征的模式分类任务的混合神经网络系统。
A hybrid neural network system for pattern classification tasks with missing features.
作者信息
Lim Chee-Peng, Leong Jenn-Hwai, Kuan Mei-Ming
机构信息
School of Electrical and Electronic Engineering, University of Science Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia.
出版信息
IEEE Trans Pattern Anal Mach Intell. 2005 Apr;27(4):648-53. doi: 10.1109/TPAMI.2005.64.
A hybrid neural network comprising Fuzzy ARTMAP and Fuzzy C-Means Clustering is proposed for pattern classification with incomplete training and test data. Two benchmark problems and a real medical pattern classification task are employed to evaluate the effectiveness of the hybrid network. The results are analyzed and compared with those from other methods.
提出一种由模糊自适应共振理论映射（Fuzzy ARTMAP）和模糊C均值聚类（Fuzzy C-Means Clustering）组成的混合神经网络，用于对不完整训练数据和测试数据进行模式分类。采用两个基准问题和一个实际医学模式分类任务来评估该混合网络的有效性。对结果进行分析，并与其他方法的结果进行比较。
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