一种提高钞票神经分类器可靠性的有效拒绝规则

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI:10.1109/NNSP.2003.1318050

A. Ahmadi, S. Omatu, T. Kosaka

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引用次数: 1

摘要

本文研究了钞票神经分类器的可靠性，提出了一种基于输入数据的概率密度函数的拒绝规则。通过两个参数来评估分类的可靠性，这两个参数与获胜类别概率和第二次最大概率有关。然后考虑一个阈值来拒绝不可靠分类。为了对数据变量之间的非线性相关性进行建模并提取特征，采用了局部主成分分析(PCA)。用学习向量量化(LVQ)分类器对该方法进行了测试，使用了3600个不同面额美元的数据样本。结果表明，选取合适的拒绝阈值和适当的区域数进行局部主成分分析，可以显著提高系统的可靠性。

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An effective reject rule for reliability improvement in bank note neuro-classifiers

In this paper the reliability of bank note neuro-classifiers is investigated and a reject rule is proposed on the basis of probability density function of the input data. The reliability of classification is evaluated through two parameters, which are associated with the winning class probability and the second maximal probability. Then a threshold value is considered to reject the unreliable classifications. As for modeling the non-linear correlation among the data variables and extracting the features, a local principal components analysis (PCA) is applied. The method is tested with a learning vector quantization (LVQ) classifier using 3,600 data samples of various bills of US dollar. The results show that by taking a suitable reject threshold value and also a proper number of regions for the local PCA, the reliability of the system can be improved significantly.

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来源期刊

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)

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