摘要
为了提高多维分类的执行效率,同时保持高的预测准确性,提出了一种基于贝叶斯网络的多维分类学习方法。将多维分类问题描述为条件概率分布问题。根据类别向量之间的依赖关系建立了条件树贝叶斯网络模型。最后,根据训练数据集对条件树贝叶斯网络模型的结构和参数进行学习,并提出了一种多维分类预测算法。大量的真实数据集实验表明,提出的方法与当前最好的多维分类算法MMOC相比,在保持高准确性的同时将模型的训练时间降低了两个数量级。因此,提出的方法更适用于海量数据的多维分类应用中。
In order to improve the execution efficiency of multi-dimensional classification while preserving high prediction accuracy,this paper proposed a Bayesian net based multi-dimensional classification learning algorithm. Firstly,it described the problem of multi-dimensional classification as the problem of conditional probability distribution. Secondly,it built a conditional tree Bayesian net model according to the dependence of class vector. Finally,it learnt the structure and parameters of the conditional tree model based on the training data set,and proposed a multi-dimensional classification prediction algorithm.Massive experiments on real dataset show that,compared with the state-of-the-art multi-dimensional classification algorithm MMOC,the proposed algorithm improves the execution efficiency of multi-dimensional classification while preserving high prediction accuracy. So,the proposed algorithm is more suitable in multi-dimensional classification for massive data.
出处
《计算机应用研究》
CSCD
北大核心
2016年第3期689-692,共4页
Application Research of Computers
基金
国家自然科学基金资助项目(61170306)
关键词
多维分类
贝叶斯网络
机器学习
海量数据
multi-dimensional classification
Bayesian network
machine learning
massive data
