Abstract The idea to exploit label dependencies for better prediction is at the core of methods multi-label classification (MLC), and performance improvements are normally explained in this way. Surprisingly, however, there no established methodology that allows analyze dependence-awareness MLC algorithms. With goal mind, we introduce a class loss functions able capture important aspect depende...

We present a loss function which can be viewed as a generalization of many popular loss functions used in robust statistics: the Cauchy/Lorentzian, Welsch, and generalized Charbonnier loss functions (and by transitivity the L2, L1, L1-L2, and pseudo-Huber/Charbonnier loss functions). We describe and visualize this loss, and document several of its useful properties. Many problems in statistics ...

Loss-averse behavior makes the newsvendors avoid the losses more than seeking the probable gains as the losses have more psychological impact on the newsvendor than the gains. In economics and decision theory, the classical newsvendor models treat losses and gains equally likely, by disregarding the expected utility when the newsvendor is loss-averse. Moreover, the use of unbounded utility to m...