摘要
朴素贝叶斯分类器(nave bayes)是一种简单而有效的基于贝叶斯思想的分类方法,但它的属性条件独立性假设并不符合实际,影响了它的分类性能。BAN(bayesian network augmented nave bayes)分类器扩展了朴素贝叶斯分类器,使其表示属性之间依赖关系的能力增强,但是其学习算法需要大量的高维计算,在小采样数据集上,影响BAN分类器的分类性能。基于改进的最大相关最小冗余特征选择技术,提出限定性贝叶斯网络分类器学习算法(k-BAN)。本算法使用改进的最大相关最小冗余特征选择技术,通过选择属性结点的连接关系集合建立属性之间的依赖性关系。将该分类方法与NB,TAN和BAN分类器进行实验比较。实验结果表明,在小采样数据集上,本算法获得的限定性贝叶斯网络分类器具有更高的分类准确性。
NB （Naive Bayes） classifier is a simple and effective classification method, which is based on Bayes theorem. However, its attribute conditional independence assumption usually doesn＇t correspond to reality, which affects its classification performance. BAN （Bayesian network Augmented Naive Bayes） classifier extends the ability to represent the dependence among attributes. However, BAN learning algorithms need a large amount of high dimensional computations, which impairs the classification accuracy of BAN,especially on small sample datasets. Based on the variant of max-relevance rain-redundancy feature selection technology, a new restrictive BAN classifier learning algorithm （k-BAN）, which builds the dependence by selecting the set of edges for each attribute node,is proposed. Compared with NB, TAN and BAN classifiers by an experiment, the restrictive BAN classifier of our algorithm has better classification accuracy,especially on small sample datasets.
出处
《重庆大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2014年第6期71-77,共7页
Journal of Chongqing University
基金
国家自然科学基金资助项目(61172168)
