An Overview of Concept Drift Applications¶
Žliobaitė, I., Pechenizkiy, M., & Gama, J. (2016). An Overview of Concept Drift Applications. Big Data Analysis: New Algorithms for a New Society.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Reference Cadence Exceeds Tracking Bandwidth
- The retraining-cadence-versus-drift logic transferred from machine learning into online-retail personalization with the identical trade-off between compute cost and drift error.
This sourceSurveys concept drift across applications including retail/personalization, framing the retraining-cadence-versus-drift trade-off (compute cost vs. staleness) that maps the bandwidth-vs-reference-cadence inequality onto deployed models.
Supported in partVerified against the publisher's abstract
The abstract states that models become obsolete as data evolve (concept drift) and that the chapter surveys application tasks where drift matters; it does not mention retail personalization or a compute-cost/drift trade-off.
“Patterns and relations in such data often evolve over time, thus, models built for analyzing such data quickly become obsolete over time. In machine learning and data mining this phenomenon is referred to as concept drift. The objective is to deploy models that would diagnose themselves and adapt to changing data over time.”
- The retraining-cadence-versus-drift logic transferred from machine learning into online-retail personalization with the identical trade-off between compute cost and drift error.
Verification¶
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Registry ID ref:a2b751edb754 · see in the full table