Adapting Supervised Classification Algorithms to Arbitrary Weak Label Scenarios

Miquel Perelló-Nieto*, Raúl Santos-Rodríguez, Jesús Cid-Sueiro

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

2 Citations (Scopus)
329 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationAdvances in Intelligent Data Analysis XVI
Subtitle of host publication16th International Symposium, IDA 2017, London, UK, October 26–28, 2017, Proceedings
PublisherSpringer Verlag
Pages247-259
Number of pages13
ISBN (Electronic)9783319687650
ISBN (Print)9783319687643
DOIs
Publication statusPublished - 4 Oct 2017
Event16th International Symposium on Intelligent Data Analysis, IDA 2017 - London, United Kingdom
Duration: 26 Oct 201728 Oct 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10584 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Symposium on Intelligent Data Analysis, IDA 2017
CountryUnited Kingdom
CityLondon
Period26/10/1728/10/17

Structured keywords

  • Digital Health

Keywords

  • Noisy labels
  • Proper losses
  • Weak labels

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