Not so naive Bayes: Aggregating one-dependence estimators
Not so naive Bayes: Aggregating one-dependence estimators
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DOI：
10.1007/s10994-005-4258-6
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发表时间：
2005-01-01
期刊：
影响因子：
7.5
通讯作者：
Wang, ZH
中科院分区：
文献类型：
--
作者：
Webb, GI;Boughton, JR;Wang, ZH
在众多通过弱化朴素贝叶斯属性独立性假设来提高朴素贝叶斯精度的建议中，LBR和超级亲本Tan都表现出了显著的错误性能。然而，这两种技术都以相当大的计算代价获得了这一结果。我们提出了一种新的方法，通过对所有受约束的分类器进行平均来弱化属性独立性假设。在广泛的实验中，该技术提供了与LBR和超级亲本TAN相当的预测精度，并且相对于前者，在测试时和在训练时相对于后者，计算效率都有很大提高。实验结果表明，该算法具有较低的方差，适合增量学习。
Of numerous proposals to improve the accuracy of naive Bayes by weakening its attribute independence assumption, both LBR and Super-Parent TAN have demonstrated remarkable error performance. However, both techniques obtain this outcome at a considerable computational cost. We present a new approach to weakening the attribute independence assumption by averaging all of a constrained class of classifiers. In extensive experiments this technique delivers comparable prediction accuracy to LBR and Super-Parent TAN with substantially improved computational efficiency at test time relative to the former and at training time relative to the latter. The new algorithm is shown to have low variance and is suited to incremental learning.