In this article, we present a novel neighbourhood based multi-label classifier, Multi-label Learning on principles of Reverse k-Nearest Neighbourhood (ML-RkNN) where we estimate the neighbourhood of the points on the basis of their reverse k-nearest neighbourhood (RkNN). Through RkNN, for the same value of k, we get different number of neighbours for different instances and this happens adaptively according to the neighbourhood configuration of the points. The automatically adaptive neighbourhood helps us in better learning of the local configurations around the points. Our scheme also facilitates implicit handling of the local imbalances prevailing in the datasets by comparing the class distributions of the test points and their reverse nearest neighbours. This implicit and adaptive handling is particularly useful for multi-label label datasets, whose labels are differentially imbalanced. Empirical study is performed on 10 real-world multi-label datasets considering five neighbourhood based multi-label learners. Macro F₁ is used as the evaluating metric. The proposed method has given statistically superior and statistically comparable performances with respect to three and two comparing methods respectively. Additionally, we have explored the use of two different distance metrics, Euclidean and Jaccard in our scheme for nominal datasets.

中文翻译：

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基于反向k-最近邻域原理的多标签学习

在本文中，我们提出了一个新颖的基于邻域多标记分类器，逆向的原理多标记学习k-最近邻域（ML-RkNN）其中我们估计其反向的基础上的点的附近ķ -nearest附近(RkNN)。通过 RkNN，对于相同的k值，我们为不同的实例获得不同数量的邻居，这根据点的邻居配置自适应地发生。自动自适应邻域帮助我们更好地学习点周围的局部配置。我们的方案还通过比较测试点及其反向最近邻点的类分布，促进了对数据集中普遍存在的局部不平衡的隐式处理。这种隐式和自适应处理对于标签差异不平衡的多标签标签数据集特别有用。考虑到五个基于邻域的多标签学习器，对 10 个真实世界的多标签数据集进行了实证研究。微距F ₁用作评估指标。所提出的方法分别针对三种和两种比较方法给出了统计上优越和统计上可比的性能。此外，我们已经探索了在我们的名义数据集方案中使用两种不同的距离度量，欧几里得和杰卡德。