article Open access

Evaluation methods and decision theory for classification of streaming data with temporal dependence

Indrė Žliobaitė^a✉, Albert Bifet^{b c}, Jesse Read^a, Bernhard Pfahringer^d, Geoffrey Holmes^d

^aDepartment of Information and Computer Science, Aalto University and Helsinki Institute for Information Technology (HIIT), Espoo, Finland
^bHuawei Noah’s Ark Research Lab, Hong Kong, China
^cHuawei Noah's Ark Research Lab, Hong Kong, China#TAB#
^dUniversity of Waikato, Hamilton, New Zealand
✉Corresponding author

Machine Learning Springer Science+Business Media2014-04-25 127 citations51 references

Looking for the full paper…

Export

Papers open inside SciRah from the original publisher or repository; SciRah never hosts them.

Abstract

No abstract is available from our sources. Use “Read paper” to view it.

TL;DRIt is suggested that the commonly accepted data stream classification measures, such as classification accuracy and Kappa statistic, fail to diagnose cases of poor performance when temporal dependence is present, therefore they should not be used as sole performance indicators. (Semantic Scholar)

Plain-language summary

AI illustration of the topic of “Evaluation methods and decision theory for classification of streaming data with temporal dependence”

Evaluation methods and decision theory for classification of streaming data with temporal dependence

Abstract

Plain-language summary

Discussion