Boosting Diverse Learners for Domain Agnostic Time Series Classification

  • Minnen D
  • Zang P
  • Isbell C
 et al. 
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Although most classification methods benefit from the in- corporation of domain knowledge, some situations call for a single algorithm that applies to a wide range of diverse do- mains. In such cases, the techniques and biases that prove useful in one domain may be irrelevant or even harmful in another. This paper addresses the problem of constructing a domain agnostic time series classification algorithm that al- lows safe inclusion of domain-specific methods that may be highly effective in some domains yet detrimental in others. Our approach combines MBoost, an extension to AdaBoost that allows robust boosting of multiple weak learners, with SAMME, a multiclass extension of AdaBoost which does not rely on a reduction to a set of binary problems. The result- ing algorithm allows the safe and efficient combination of multiple learning algorithms for multiclass classification.

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  • David Minnen

  • P Zang

  • Charles Isbell

  • Thad Starner

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