id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_qtojh7qkjvfmjcxu45bmddfw3y	Hyukjun Gweon	Nearest labelset using double distances for multi-label classification	2019	20	.pdf	application/pdf	9439	1385	70	labelset observed in the training data that minimizes a weighted sum of the distances in Experiments on benchmark multi-label data sets show that the proposed method on Nearest labelset using double distances for multi-label classification. The effectiveness of the proposed approach is evaluated with various multi-label data sets. A multi-label training data set is That is, NLDD predicts by choosing the labelset of the training instance that minimizes the Input: new instance x, binomial model g, probabilistic classifiers h(i), training data T of validation data set, T2.1 We next fit a binary classifier to each of the L labels separately In this section we compare different multi-label algorithms on nine data sets. We evaluated the proposed approach using nine commonly used multi-label data sets from Table 1 Multi-label data sets and their associated characteristics. true labelsets of the test instances were observed in the training data (subset A), NLDD	./cache/work_qtojh7qkjvfmjcxu45bmddfw3y.pdf	./txt/work_qtojh7qkjvfmjcxu45bmddfw3y.txt