改进朴素贝叶斯分类算法的研究与应用  

Research and Application of Improved Naive Bayesian Classification Algorithm

作　　者：吕昊[1] 林君[1] 曾晓献[1] 

机构地区：[1]吉林大学仪器科学与电气工程学院,吉林长春130026

出　　处：《湖南大学学报（自然科学版）》2012年第12期56-61,共6页Journal of Hunan University:Natural Sciences

基　　金：国家自然科学基金资助项目(41074082)

摘　　要：由于生成参数学习方法以极大似然性为目标,从而导致分类精度较低.针对这一问题,本文提出了一种以最大化分类精度为目标的高效判别参数学习方法.该方法通过在频率估计方法中加入一个判别参数,从而判别性地计算参数的出现频率,加强实例属性与分类类别之间的关联性.在UCI数据集上的实验表明,该方法综合了生成学习与判别学习的优点,分类精度与目前主流的SVM算法相当,但是在训练时间上具有明显的优势.最后将本方法应用于油水层模式识别当中,其分类性能优于其他算法.As generative parameter learning method uses the maximum likelihood as the target,its classification accuracy is low.So,we proposed an efficient discriminative parameter learning algorithm,which uses the classification accuracy as the target.It learns parameters by discriminatively computing the frequencies of parameters from data set.Empirical studies show that this algorithm integrates the advantages of both generative and discriminative learning and it performs as well as the state-of-art classification method SMV,but is significantly more efficient.At last,this method is used in the problem to identify oil and water layers.The accuracy of the conclusion has very important value for oil field development and production.

关 键 词：数据挖掘 油田开发 朴素贝叶斯分类 判别参数学习 油水层识别 储层评估 

分 类 号：TE319[石油与天然气工程—油气田开发工程]