Local value difference metric
Local value difference metric
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局部值差异度量
DOI：
10.1016/j.patrec.2014.06.014
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发表时间：
2014-11-01
影响因子：
5.1
通讯作者：
Li, Hongwei
中科院分区：
文献类型：
--
作者：
Li, Chaoqun;Jiang, Liangxiao;Li, Hongwei
值差度量 (VDM) 是广泛使用的距离函数之一，旨在处理标称属性。研究表明，VDM 的定义自然地遵循称为朴素贝叶斯 (NB) 的简单概率模型。注意假设所有属性在给定的类中都是独立的。为了进一步提高NB的性能，已经提出了几种技术。其中，一种有效的技术是本地学习。由于VDM与NB有着密切的关系，因此在本文中，我们提出了一种VDM的局部学习方法。改进的距离函数称为局部值差度量（LVDM）。当 LVDM 计算测试实例和每个训练实例之间的距离时，VDM 中的条件概率是通过仅从测试实例的邻域计数而不是从所有训练数据进行计数来估计的。提出了一种改进的决策树算法来确定测试实例的邻域。从加州大学欧文分校（UCI）下载的 43 个数据集上的实验结果表明，在基于距离的学习算法的类概率估计性能方面，所提出的 LVDM 显着优于 VDM。 (C) 2014 Elsevier B.V. 保留所有权利。
Value difference metric (VDM) is one of the widely used distance functions designed to work with nominal attributes. Research has indicated that the definition of VDM follows naturally from a simple probabilistic model called a naive Bayes (NB). NB assumes that all the attributes are independent given the class. To further improve the performance of NB, several techniques have been proposed. Among these, an effective technique is local learning. Because VDM has a close relationship with NB, in this paper, we propose a local learning method for VDM. The improved distance function is called local value difference metric (LVDM). When LVDM computes the distance between a test instance and each training instance, the conditional probabilities in VDM are estimated by counting from the neighborhood of the test instance only instead of from all the training data. A modified decision tree algorithm is proposed to determine the neighborhood of the test instance. The experimental results on 43 datasets downloaded from the University of California at Irvine (UCI) show that the proposed LVDM significantly outperforms VDM in terms of the class-probability estimation performance of distance-based learning algorithms. (C) 2014 Elsevier B.V. All rights reserved.