Boosting Diverse Learners for Domain Agnostic Time Series Classification
BibTeX
@MISC{Minnen_boostingdiverse,
author = {David Minnen and Peng Zang and Charles Isbell and Thad Starner},
title = {Boosting Diverse Learners for Domain Agnostic Time Series Classification},
year = {}
}
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Abstract
Although most classification methods benefit from the incorporation of domain knowledge, some situations call for a single algorithm that applies to a wide range of diverse domains. 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 allows 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 resulting algorithm allows the safe and efficient combination of multiple learning algorithms for multiclass classification. 1.
