Bayesian Citation-KNN with distance weighting
Bayesian Citation-KNN with distance weighting
复制标题
具有距离加权的贝叶斯引用-KNN
DOI：
10.1007/s13042-013-0152-x
复制
发表时间：
2014-04-01
影响因子：
5.6
通讯作者：
Zhang, Harry
中科院分区：
文献类型：
--
作者：
Jiang, Liangxiao;Cai, Zhihua;Zhang, Harry
多实例(MI)学习在机器学习领域受到越来越多的关注，其中学习实例由单个实例表示为一包实例。K-近邻(KNN)是传统监督学习中一种简单有效的分类模型。贝叶斯-KNN(BKNN)和引文-KNN(CKNN)是贝叶斯-KNN的两个变种，被广泛应用于解决多实例分类问题。然而，CKNN仍然使用参考文献中最简单的多数票方法来对看不见的袋子进行分类。本文提出了一种改进的贝叶斯引用KNN(BCKNN)算法。对于每个看不见的包，BCKNN首先分别找到它的参考文献和引文，然后对它的参考文献应用贝叶斯方法，对它的分类器应用距离加权多数投票方法。在几个基准数据集上的实验结果表明，我们的BCKNN总体上要好于以前的BKNN和CKNN。此外，BCKNN几乎保持了与CKNN相同的计算开销。
Multi-instance (MI) learning is receiving growing attention in the machine learning research field, in which learning examples are represented by a bag of instances instead of a single instance. K-nearest-neighbor (KNN) is a simple and effective classification model in the traditional supervised learning. As its two variants, Bayesian-KNN (BKNN) and Citation-KNN (CKNN) are proposed and are widely used for solving multi-instance classification problems. However, CKNN still applies the simplest majority vote approach among the references and citers to classify unseen bags. In this paper, we propose an improved algorithm called Bayesian Citation-KNN (BCKNN). For each unseen bag, BCKNN firstly finds itsreferences andciters respectively, and then a Bayesian approach is applied to itsreferences and a distance weighted majority vote approach is applied to itsciters. The experimental results on several benchmark datasets show that our BCKNN is generally better than previous BKNN and CKNN. Besides, BCKNN almost maintains the same order of computational overhead as CKNN.