Genetic algorithms for spectral pattern recognition
Genetic algorithms for spectral pattern recognition
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DOI：
10.1016/s0924-2031(01)00147-3
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发表时间：
2002-02-28
影响因子：
2.5
通讯作者：
Moores, AJ
中科院分区：
文献类型：
--
作者：
Lavine, BK;Davidson, CE;Moores, AJ
报道了一种用于光谱数据模式识别分析的遗传算法。GA识别一组特征(波长)，这些特征(波长)在数据的两个或三个最大主成分的曲线图中优化类的分离。由于主成分最大化方差，因此由所选特征编码的大部分信息与数据集中的类之间的差异有关。此外，遗传算法将重点放在那些难以分类的类别和/或样本上，因为它使用一种助推的形式进行训练，以修改健康状况。Booking使收敛到局部最优的问题最小化，因为随着种群向解的进化，GA的适应度函数也在变化。随着时间的推移，始终正确分类的样本在分析中的权重不像难以分类的样本那么重。模式识别GA以类似于神经网络的方式学习其最优参数。该算法综合了强学习和弱学习的特点，形成了一种“智能”的一次通过过程。(C)2002 Elsevier Science B.V.保留所有权利。
The development of a genetic algorithm (GA) for pattern recognition analysis of spectral data is reported. The GA identifies a set of features (wavelengths) that optimize the separation of the classes in a plot of the two or three largest principal components of the data. Because principal components maximize variance, the bulk of the information encoded by the selected features is about differences between classes in the dataset. In addition, the GA focuses on those classes and or samples that are difficult to classify as it trains using a form of boosting to modify the fitness landscape. Boosting minimizes the problem of convergence to a local optimum since the fitness function of the GA is changing as the population is evolving towards a solution. Over time, samples that consistently classify correctly are not as heavily weighted in the analysis as samples that are difficult to classify. The pattern recognition GA learns its optimal parameters in a manner similar to a neural network. The algorithm integrates aspects of both strong and weak learning to yield a "smart" one-pass procedure. (C) 2002 Elsevier Science B.V. All rights reserved.