DISCRIMINATIVELY WEIGHTED NAIVE BAYES AND ITS APPLICATION IN TEXT CLASSIFICATION
DISCRIMINATIVELY WEIGHTED NAIVE BAYES AND ITS APPLICATION IN TEXT CLASSIFICATION
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判别加权朴素贝叶斯及其在文本分类中的应用
DOI：
10.1142/s0218213011004770
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发表时间：
2012-02-01
影响因子：
1.1
通讯作者：
Cai, Zhihua
中科院分区：
文献类型：
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作者：
Jiang, Liangxiao;Wang, Dianghong;Cai, Zhihua
许多方法被提出来通过削弱其条件独立性假设来改进朴素贝叶斯。本文研究了实例加权的方法，提出了一种改进的朴素贝叶斯算法。我们称之为判别加权朴素贝叶斯。在每次迭代中，根据估计的条件概率损失，有区别地为不同的训练实例分配不同的权重。基于大量UCI数据集的实验结果验证了其分类准确率和曲线下面积的有效性。此外，在运行时间上的实验结果表明，我们的判别加权朴素贝叶斯的性能几乎与最先进的判别频率估计学习方法一样有效，并且比Boosted Naive Bayes更有效。最后，我们将算法中的区分加权学习思想应用于一些最先进的朴素贝叶斯文本分类器，例如多项式朴素贝叶斯、互补朴素贝叶斯和一对多除一模型，并取得了显着的改进。
Many approaches are proposed to improve naive Bayes by weakening its conditional independence assumption. In this paper, we work on the approach of instance weighting and propose an improved naive Bayes algorithm by discriminative instance weighting. We called it Discriminatively Weighted Naive Bayes. In each iteration of it, different training instances are discriminatively assigned different weights according to the estimated conditional probability loss. The experimental results based on a large number of UCI data sets validate its effectiveness in terms of the classification accuracy and AUC. Besides,the experimental results on the running time show that our Discriminatively Weighted Naive Bayes performs almost as efficiently as the state-of-the-art Discriminative Frequency Estimate learning method, and significantly more efficient than Boosted Naive Bayes. At last, we apply the idea of discriminatively weighted learning in our algorithm to some state-of-the-art naive Bayes text classifiers, such as multinomial naive Bayes, complement naive Bayes and the one-versus-all-but-one model, and have achieved remarkable improvements.