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Learning Scoring Functions with Order-Preserving Losses and Standardized Supervision
Abstract : We address the problem of designing surrogate losses for learning scoring functions in the context of label ranking. We extend to ranking problems a notion of order preserving losses previously introduced for multiclass classi?cation, and show that these losses lead to consistent formulations with respect to a family of ranking evaluation metrics. An order-preserving loss can be tailored for a given evaluation metric by appropriately setting some weights depending on this metric and the observed supervision. These weights, called the standard form of the supervision, do not always exist, but we show that previous consistency results for ranking were proved in special cases where they do. We then evaluate a new pairwise loss consistent with the (Normalized) Discounted Cumulative Gain on benchmark datasets.
Cited literature [18 references]
https://hal.archives-ouvertes.fr/hal-00834045
Contributor : Clément Calauzènes Connect in order to contact the contributor
Submitted on : Friday, June 14, 2013 - 9:03:01 AM
Last modification on : Wednesday, January 12, 2022 - 3:47:18 AM
Long-term archiving on: : Sunday, September 15, 2013 - 4:10:09 AM
Contributor : Clément Calauzènes Connect in order to contact the contributor
Submitted on : Friday, June 14, 2013 - 9:03:01 AM
Last modification on : Wednesday, January 12, 2022 - 3:47:18 AM
Long-term archiving on: : Sunday, September 15, 2013 - 4:10:09 AM
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