基于正则化网络-遗传算法的属性筛选及其在化学模式识别中的应用

采用贝叶斯正则化方法训练，以得到推广性优良的神经网络，并提出启发性的遗传算法。通过灵敏度分析对正则化网络实施剪枝，从而在高维模式中筛选出能代表其分类特性的最小最优属性特征子集。此方法应用于高维留兰香模式的属性筛选与模式分类，效果良好，明显优于其它方法。

Attribute Selection Based on Regularization Networks-Genetic Algorithm and Its Application in Chemical Pattern Recognition

The Bayes regularization method is employed to get a well generalized neural networks, the redundant and irrelevant attributes can be deleted from the prime attributes set by pruning the input node of the networks. In order to reduce the complexity of searching optimal subset, by the classification accuracy and fitting error of networks used as the first and second fitness value separately, we present a heuristic genetic algorithm to prune the networks by the sensitivity analysis, and the minimum with optimal attributes subset which represents the characteristic of classification can be selected from the patterns of high dimensionality. Finally, the problem of attribute selection and patterns classification of spearmint essence are employed to verify the validity of this method, the result shows that the method is superior to other methods obviously, and the method is also useful in chemical data mining.