Multi-label classification (MLC) has recently received increasing interest from the machine learning community. Several studies provide reviews of methods and datasets for MLC and a few provide empirical comparisons of MLC methods. However, they are limited in the number of methods and datasets considered. This work provides a comprehensive empirical study of a wide range of MLC methods on a plethora of datasets from various domains. More specifically, our study evaluates 26 methods on 42 benchmark datasets using 20 evaluation measures. The adopted evaluation methodology adheres to the highest literature standards for designing and executing large scale, time-budgeted experimental studies. First, the methods are selected based on their usage by the community, assuring representation of methods across the MLC taxonomy of methods and different base learners. Second, the datasets cover a wide range of complexity and domains of application. The selected evaluation measures assess the predictive performance and the efficiency of the methods. The results of the analysis identify RFPCT, RFDTBR, ECCJ48, EBRJ48 and AdaBoost.MH as best performing methods across the spectrum of performance measures. Whenever a new method is introduced, it should be compared to different subsets of MLC methods, determined on the basis of the different evaluation criteria.

中文翻译：

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多标签分类方法的综合比较研究

最近，多标签分类（MLC）受到了机器学习社区的越来越多的关注。一些研究提供了有关MLC方法和数据集的评论，还有一些研究提供了MLC方法的经验比较。但是，它们在所考虑的方法和数据集的数量上受到限制。这项工作为来自不同领域的大量数据集上的多种MLC方法提供了全面的经验研究。更具体地说，我们的研究使用20种评估方法对42个基准数据集评估了26种方法。所采用的评估方法遵循最高的文献标准，用于设计和执行大规模的，预算有限的实验研究。首先，根据社区使用情况选择方法，确保在方法和不同基础学习者的MLC分类中代表方法。其次，数据集涵盖了广泛的复杂性和应用领域。选择的评估方法评估方法的预测性能和效率。分析结果确定RFPCT，RFDTBR，ECCJ48，EBRJ48和AdaBoost.MH是在整个性能指标范围内表现最佳的方法。每当引入新方法时，应将其与根据不同评估标准确定的MLC方法的不同子集进行比较。EBRJ48和AdaBoost.MH是整个性能指标中性能最好的方法。每当引入新方法时，应将其与根据不同评估标准确定的MLC方法的不同子集进行比较。EBRJ48和AdaBoost.MH是整个性能指标中性能最好的方法。每当引入新方法时，应将其与根据不同评估标准确定的MLC方法的不同子集进行比较。