Abstract
In many real-world problems, labels are often weak, meaning that each instance is labelled as belonging to one of several candidate categories, at most one of them being true. Recent theoretical contributions have shown that it is possible to construct proper losses or classification calibrated losses for weakly labelled classification scenarios by means of a linear transformation of conventional proper or classification calibrated losses, respectively. However, how to translate these theoretical results into practice has not been explored yet. This paper discusses both the algorithmic design and the potential advantages of this approach, analyzing consistency and convexity issues arising in practical settings, and evaluating the behavior of such transformations under different types of weak labels.
JCS is supported by the TEC2014-52289-R project funded by the Spanish MEC. MPN and RSR are supported by the SPHERE IRC funded by the UK Engineering and Physical Sciences Research Council (EP/K031910/1).
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Perelló-Nieto, M., Santos-Rodríguez, R., Cid-Sueiro, J. (2017). Adapting Supervised Classification Algorithms to Arbitrary Weak Label Scenarios. In: Adams, N., Tucker, A., Weston, D. (eds) Advances in Intelligent Data Analysis XVI. IDA 2017. Lecture Notes in Computer Science(), vol 10584. Springer, Cham. https://doi.org/10.1007/978-3-319-68765-0_21
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