Computing Outlier Thresholds under Interval Uncertainty

  • Hung T. Nguyen
  • Vladik Kreinovich
  • Berlin Wu
  • Gang Xiang
Part of the Studies in Computational Intelligence book series (SCI, volume 393)

Abstract

In many application areas, it is important to detect outliers. The traditional engineering approach to outlier detection is that we start with some “normal” values x1,..., x n , compute the sample average E, the sample standard variation σ, and then mark a value x as an outlier if x is outside the k0-sigma interval [Ek0 · σ, E + k0 · σ] (for some pre-selected parameter k0). In real life, we often have only interval ranges [\(\underline{x}_{i}\), \(\overline{x}_{i}\)] for the normal values x1,..., x n . In this case, we only have intervals of possible values for the “outlier threshold” – bounds Ek0 · σ and E + k0 · σ. We can therefore identify outliers as values that are outside all k0-sigma intervals.

Keywords

Common Point Outlier Detection Sharp Bound Actual Range Constraint Satisfaction Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hung T. Nguyen
    • Vladik Kreinovich
      • Berlin Wu
        • Gang Xiang

          There are no affiliations available

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