基于优化 LM 模糊神经网络的不均衡林业信息文本分类算法

摘要

为解决不均衡林业信息文本分类中少数类分类正确率低问题，提出了一种基于优化 LM 模糊神经网络的不均衡林业信息文本分类算法。在阐述优化 LM 模糊神经网络算法原理的基础上，提取不均衡林业信息文本特征矩阵训练分类器的各项参数，实现对不均衡林业信息文本的精准与快速分类。实验结果表明该算法对少数类辨识准确率高，优于神经网络分类法以及 SVM 算法、模糊神经网络算法，为不均衡林业信息文本的分类提供了新思路。%In order to deal with the problem of low categorization accuracy of minority class of the uneven forestry information text classification algorithm, the uneven forestry information text classification algorithm was puts forward based on optimization LM fuzzy neural network (OLM-FNN). On the basis of expounding the principle of optimization LM fuzzy neural network (FNN), the parameters feature matrix training classifier of uneven forestry information text to of LM fuzzy neural network were extracted, thus realizing accurate and fast classification to uneven forestry information text. The experimental results show that the algorithm had higher classification accuracy of minority class than that of neural network and support vector machine (SVM) and fuzzy neural network. The algorithm provides new ideas for studying on uneven forestry information text classification algorithm.