Estimating Reliability of Contextual Evidences in Decision-List Classifiers under Bayesian Learning
Estimating Reliability of Contextual Evidences in Decision-List Classifiers under Bayesian Learning
复制标题
贝叶斯学习下决策列表分类器中上下文证据的可靠性估计
DOI：
--
复制
发表时间：
2001
期刊：
影响因子：
--
通讯作者：
T. Chikayama
中科院分区：
文献类型：
--
作者：
Yoshimasa Tsuruoka;T. Chikayama
分类器通常不仅需要输出分类结果，而且需要输出分类的概率。我们专注于已成功应用于各种NLP任务的决策列表分类器。我们提出了基于贝叶斯学习的方法来计算决策列表中上下文证据的可靠性，这使得决策列表能够输出理论上有根据的概率。在Senseval-1数据集上的实验结果表明，该方法能够使决策表输出正确反映其可靠性的概率，提高了决策表算法的分类性能.
Classifiers are often required to output not only a classification result but also the probability of the classification. We focus on the decision list classifier which has successfully been applied to a wide variety of NLP tasks. We propose methods based on Bayesian learning to calculate the reliability of contextual evidences in decision lists, which enables decision lists to output theoretically well-founded probabilities. Experimental results obtained using Senseval-1 data set show that our methods enable decision lists to output probabilities appropriately reflecting their reliabilities and improve the classification performance of the decision list algorithm.