优化分类型神经网络线性集成
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Supported by the National Natural Science Foundation of China under Grant No.602730333(国家自然科学基金);the Natural Science Foundation of Jiangsu Province of China under Grant No.BK2004079(江苏省自然科学基金);the Natural Science Foundation of Yangzhou University of China under Grant No.KK0413160(扬州大学自然科学基金)


An Optimized Neural Network Linear Ensemble for Classification
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    摘要:

    构造多神经网络集成系统,系统的输出由个体神经网络的输出线性加权产生.提出了一种度量个体神经网络在不同的权重下集成性能的判别函数,函数表示了由个体神经网络输出刻画的模式类内会聚性和类间散布性.应用遗传算法解决了求解最优个体网络集成权重问题.分析了该判别函数的合理性及其与Bayes决策规则的关系.用两个手写体汉字特征数据集和4个UCI数据库中的数据集比较了所提出的神经网络集成方法和其他几种神经网络集成方法的性能.

    Abstract:

    Neural network ensembles have been used increasingly in recent years to improve classifier’s generalization ability. There are several methods of designing neural network ensembles, such as weighted average linear ensemble. The output of the weighted average linear ensemble is a weighted average of the output of each component neural network, with the weights determined by a function. Based on the characteristic of the classification issue, a function is defined, which is the ratio of the pattern separability within class to the pattern scatter between classes. The minimum of the function corresponds to the optimal weights, so an optimal linear ensemble is obtained. The optimal weights are searched by a genetic algorithm. The rationale behind the function is also analyzed, showing that it accords with the Bayesian decision rule. Finally, to estimate the performance of this linear ensemble, two handwritten Chinese character feature sets and four data sets from UCI machine learning depository are used. Empirical study shows that the optimal linear ensemble method can produce ensemble of the neural network with a stronger generalization ability.

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王正群,陈世福,陈兆乾.优化分类型神经网络线性集成.软件学报,2005,16(11):1902-1908

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  • 收稿日期:2004-03-18
  • 最后修改日期:2005-02-04
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