Distinguishing mislabeled data from correctly labeled data in classifier design

Sundara Venkataraman, Dimitris Metaxas, Dmitriy Fradkin, Casimir Kulikowski, Ilya Muchnik

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

We have developed a method for distinguishing between correctly labeled and mislabeled data sampled from video sequences and used in the construction of a facial expression recognition classifier. The novelty of our approach lies in training a single, optimal classifier type (a Support Vector Machine, or SVM) on multiple representations of the data, involving different "discriminating" subspaces. Results of a preliminary study on the discrimination of "high stress" vs. "low stress" facial expression data by this method confirms that our novel approach is able to distinguish subproblems where labeling is highly reliable from those where mislabeling can lead to high error rates. In helping detect data sub-samples which yield misleading classification results, the method is also a rapid, highly efficient cross-validated approach for eliminating outliers.

Original languageEnglish (US)
Title of host publicationProceedings - 16th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2004
EditorsT.M. Khoshgoftaar
Pages668-672
Number of pages5
DOIs
StatePublished - 2004
EventProceedings - 16th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2004 - Boca Raton, FL, United States
Duration: Nov 15 2004Nov 17 2004

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
ISSN (Print)1082-3409

Other

OtherProceedings - 16th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2004
CountryUnited States
CityBoca Raton, FL
Period11/15/0411/17/04

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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