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首页> 外文会议>International Conference on Computational Science and Its Applications(ICCSA 2004) pt.4; 20040514-20040517; Assisi; IT >A Weighted Fuzzy Min-Max Neural Network for Pattern Classification and Feature Extraction
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A Weighted Fuzzy Min-Max Neural Network for Pattern Classification and Feature Extraction

机译：加权模糊最小-最大神经网络的模式分类与特征提取

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摘要

In this paper a modified fuzzy min-max neural network model for pattern classification and feature extraction is described. We define a new hy-percube membership function which has a weight factor to each of the feature within a hyperbox. The weight factor makes it possible to consider the degree of relevance of each feature to a class during the classification process. Based on the proposed model, a knowledge extraction method is presented. In this method, a list of relevant features for a given class is extracted from the trained network using the hyperbox membership functions and connection weights. For this purpose we define a Relevance Factor that represents a degree of relevance of a feature to the given class and a similarity measure between fuzzy membership functions of the hyperboxes. Experimental results for the proposed methods and discussions are presented for the evaluation'of the effectiveness and feasibility of the proposed methods.
机译：本文描述了一种改进的模糊最小-最大神经网络模型，用于模式分类和特征提取。我们定义了一个新的超立方体隶属度函数，该函数具有对超级框内每个功能的权重因子。权重因子使得可以在分类过程中考虑每个要素与类的相关程度。基于提出的模型，提出了一种知识提取方法。在这种方法中，使用超级框隶属函数和连接权重，从受过训练的网络中提取给定类的相关功能列表。为此，我们定义了一个相关因子，它表示要素与给定类的相关程度以及超框的模糊隶属函数之间的相似性度量。提出了所提方法的实验结果并进行了讨论，以评估所提方法的有效性和可行性。

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