Machine Learning
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Published By Springer-Verlag

1573-0565, 0885-6125

2022 ◽  
Author(s):  
Mahdi Abolghasemi ◽  
Rob J. Hyndman ◽  
Evangelos Spiliotis ◽  
Christoph Bergmeir
Keyword(s):  

2022 ◽  
Author(s):  
Shufang Xie ◽  
Yingce Xia ◽  
Lijun Wu ◽  
Yiqing Huang ◽  
Yang Fan ◽  
...  

2022 ◽  
Author(s):  
Eyke Hüllermeier ◽  
Marcel Wever ◽  
Eneldo Loza Mencia ◽  
Johannes Fürnkranz ◽  
Michael Rapp

AbstractThe idea to exploit label dependencies for better prediction is at the core of methods for multi-label classification (MLC), and performance improvements are normally explained in this way. Surprisingly, however, there is no established methodology that allows to analyze the dependence-awareness of MLC algorithms. With that goal in mind, we introduce a class of loss functions that are able to capture the important aspect of label dependence. To this end, we leverage the mathematical framework of non-additive measures and integrals. Roughly speaking, a non-additive measure allows for modeling the importance of correct predictions of label subsets (instead of single labels), and thereby their impact on the overall evaluation, in a flexible way. The well-known Hamming and subset 0/1 losses are rather extreme special cases of this function class, which give full importance to single label sets or the entire label set, respectively. We present concrete instantiations of this class, which appear to be especially appealing from a modeling perspective. The assessment of multi-label classifiers in terms of these losses is illustrated in an empirical study, clearly showing their aptness at capturing label dependencies. Finally, while not being the main goal of this study, we also show some preliminary results on the minimization of this parametrized family of losses.


2022 ◽  
Author(s):  
Federico Cerutti ◽  
Lance M. Kaplan ◽  
Angelika Kimmig ◽  
Murat Şensoy

2022 ◽  
Author(s):  
Xueqing Wu ◽  
Yingce Xia ◽  
Jinhua Zhu ◽  
Lijun Wu ◽  
Shufang Xie ◽  
...  

2022 ◽  
Author(s):  
Trung Le ◽  
Khanh Nguyen ◽  
Dinh Phung
Keyword(s):  

2022 ◽  
Author(s):  
Chi Zhang ◽  
Benyi Hu ◽  
Yuhang Liuzhang ◽  
Le Wang ◽  
Li Liu ◽  
...  

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