多种分类器在农用地分等中的应用及其用法改良

以广东省第二次土壤普查成果资料为主要数据源,选取贝叶斯决策、BP神经网络、概率神经网络、聚类等分类方法分别对数据源进行分类;并且,笔者为了充分利用有监督学习分类准确率高和无监督学习无需标定的学习样本的优点,提出了基于监督--非监督的聚类算法,然后对上述五种方法的评价结果作了比较分析;实验表明文章提出的基于监督--非监督聚类方法只利用少量的有标定学习样本,即可得到较高的分类准确率,特别在少量样本时,该方法能得到比贝叶斯决策方法、BP神经网络和概率神经网络等监督学习方法更好的土地评价结果;在实际应用中,可以尝试结合监督和非监督学习的方法,实现分类正确率和获取大量有类标签的样本之间的折中。

Multi-Classifier Application and Improved Method Research in the Land Evaluation

In this paper, the Byes method, Backpropagation Neural Network(BP),Probabilistic Neural Network(PNN) method and Clustering method are used to classify the data rooted from the data of the second earth investigation result in Guangdong province. Moreover, based-on supervised-unsupervised cluster algorithm is presented, in order to take full advantage of high accuracy of supervised learning classification and no necessity demarcated study samples. Then, the estimation result of the five methods above is compared and analyzed. According to the experiment, the gaining of higher classification accuracy just needs a few of demarcated study samples if based-on supervised-unsupervised learning is used. And especially, better earth estimation result than the Byes method ,BP Network, PNN method and so on, can be gotten when a few of samples are used. In land evaluation, can combine supervised and unsupervised learning method,implement a tradeoff between classification accuracy and expense.