Neighbor selection for multilabel classification
Neighbor selection for multilabel classification
复制标题
多标签分类的邻居选择
DOI：
10.1016/j.neucom.2015.12.035
复制
发表时间：
2016-03-19
期刊：
影响因子：
6
通讯作者：
Zhang, Shichao
中科院分区：
文献类型：
--
作者：
Liu, Huawen;Wu, Xindong;Zhang, Shichao
文献中对kNN进行了广泛的多标签分类研究。在过去的几年中，已经出现了几种基于knn的多标签学习算法。它们通常以kNN作为基分类器构建分类模型，然后利用贝叶斯规则或多数规则预测类标签。提出了一种用于多标签分类的最近邻选择方法。具体来说，利用shelly最近邻的概念，利用相关可靠的数据来预测新数据的目标标签。为了提高有效性，进一步采用确定性因子，很好地解决了数据不平衡和不确定的问题。在10个基准数据集上与11种常用的多标签分类器进行了对比实验。实验结果表明，该方法具有一定的竞争力，在大多数情况下优于常用的多标签分类器。(C) 2015 Elsevier B.V.版权所有
kNN is extensively studied for multilabel classification in the literature. Several kNN-based multilabel learning algorithms have been witnessed during the past years. They usually take kNN as their base classifiers to construct classification models, and then predict the class labels by virtue of Bayesian or majority rules. In this paper, a nearest neighbor selection for multilabel classification is proposed. Specifically, the target labels of new data are predicted with the help of those relevant and reliable data, which explored by the concept of shelly nearest neighbor. For effectiveness, the certainty factor is further adopted to well address the problem of unbalanced and uncertain data. The comparison experiments with eleven popular multilabel classifiers are conducted on ten benchmark data sets. The experimental results show that the performance of the proposed method is competitive and outperforms the popular multilabel classifiers in most cases. (C) 2015 Elsevier B.V. All rights reserved.