New Taxonomy for Autonomous Drift Learning in Data Streams
A research paper proposes a three-dimensional taxonomy for autonomous drift learning in data streams, moving beyond traditional concept drift approaches. The taxonomy categorizes drift based on system operational state: time stream drift (stochastic vs. rhythmic patterns) and data stream drift (representation drift vs. semantic changes). The work addresses the inadequacy of stationarity assumptions in complex autonomous systems.
Key facts
- The paper is arXiv:2605.01295v1.
- It introduces a three-dimensional taxonomy for drift learning.
- Time stream drift distinguishes stochastic arbitrary patterns from structural rhythmic dynamics.
- Data stream drift separates representation drift from semantic changes.
- The research challenges the assumption of stationarity in autonomous learning systems.
- Traditional concept drift focuses only on temporal shifts.
- The taxonomy is based on the operational state of the system.
- The paper proposes evolving beyond temporal non-stationarity.
Entities
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