Learning and evaluation in presence of Non-i.i.d. label noise

Nico Görnitz, Anne K. Porbadnigk, Alexander Binder, Claudia Sannelli, Mikio Braun, Klaus Muller, Marius Kloft

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


In many real-world applications, the simplified assumption of independent and identically distributed noise breaks down, and labels can have structured, systematic noise. For example, in brain-computer interface applications, training data is often the result of lengthy experimental sessions, where the attention levels of participants can change over the course of the experiment. In such application cases, structured label noise will cause problems because most machine learning methods assume independent and identically distributed label noise. In this paper, we present a novel methodology for learning and evaluation in presence of systematic label noise. The core of which is a novel extension of support vector data description/one-class SVM that can incorporate latent variables. Controlled simulations on synthetic data and a real-world EEG experiment with 20 subjects from the domain of brain-computer-interfacing show that our method achieves accuracies that go beyond the state of the art.

Original languageEnglish
Pages (from-to)293-302
Number of pages10
JournalJournal of Machine Learning Research
Publication statusPublished - 2014

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability


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