CAJ | 학술논문

将聚类技术和遗传算法相结合, 提出一种基于相似度传播算法和遗传算法的神经网络集成方法应用于手写数字识别问题. 先分别利用主成分分析和Fisher线性判别分析对数据集进行特征提取, 得到两类特征数据集, 再利用Bagging方法分别为这两类特征数据集训练简单的BP神经网络, 然后采用相似度传播算法对这些BP神经网络进行聚类, 找到作为类簇中心的网络(中心网络), 最后利用遗传算法对所有中心网络的权值进行训练, 将中心网络进行加权线性集成作为最终分类器. 在标准手写数字数据集MNIST上进行测试的实验结果表明, 该方法的识别率优于单个神经网络的识别率, 并兼顾了分类效率.
장취류기술화유전산법상결합, 제출일충기우상사도전파산법화유전산법적신경망락집성방법응용우수사수자식별문제. 선분별이용주성분분석화Fisher선성판별분석대수거집진행특정제취, 득도량류특정수거집, 재이용Bagging방법분별위저량류특정수거집훈련간단적BP신경망락, 연후채용상사도전파산법대저사BP신경망락진행취류, 조도작위류족중심적망락(중심망락), 최후이용유전산법대소유중심망락적권치진행훈련, 장중심망락진행가권선성집성작위최종분류기. 재표준수사수자수거집MNIST상진행측시적실험결과표명, 해방법적식별솔우우단개신경망락적식별솔, 병겸고료분류효솔.
The authors proposed a method for constructing neural network ensemble based on affinity propagation and genetic algorithm, which can be applied to handwritten digit recognition. Firstly, we extracted features of samples from the MNIST handwritten digit database by PCA (Principle Component Analysis) and Fisher LDA (Linear Discriminant Analysis) respectively. Secondly, we adopted the bagging method to train some BP neural networks as candidate networks using the extracted PCA and LDA feature sets. Thirdly, affinity propagation algorithm was used to group candidate networks and find exemplars in each cluster. Finally, we optimized these weights for exemplars using genetic algorithm and integrate exemplars with the optimized weights to obtain the final classifier. Experimental results on the MNIST handwritten digit database prove that the accuracy of the algorithm proposed in this paper is far higher than that of individual network while its efficiency is acceptable.