朴素贝叶斯分类器在地形评估中的应用方法

A Naive Bayes Classifier Method on Terrain Evaluation

针对目前流行的评估方法的缺点以及实际问题的具体情况,提出将朴素贝叶斯分类器应用在地形评估中。具体方法是从用专家函数评估的数据库中提取训练样本,通过基于分布熵最小原则进行特征约减,再基于最优性条件进行属性离散化,最后基于共轭分布进行参数学习得到一个的分类器。待分类样本可以直接由贝叶斯分类器得出分类结果,并且根据增量学习理论,将分类结果作为训练新的分类器的训练样本,可以进一步提高分类精度。试验表明该方法的应用减少了评估时间,并且分类精度也令人满意。

According to the disadvantage in traditional evaluation methods and to solve practical problems of terrainevaluation,a Na?觙ve Bayes Classifier method is proposed.Firstly,drawing training data produced by traditional method from the database;secondly,reducing features based on minimum entropy condition distribution criteria;thirdly,dispersing features based on optimality criteria;lastly,learning parameters based on conjugated feature distribution criteria.Thus,the Na?觙ve Bayes Classifier by which the data can be classified is obtained.According to the augmented learning theory,the classified results may be added to the train data sets to enhance the precision of the classifier.Experiments show that the new method save the classification time remarkably,and the accuracy is satisfying.