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iHOMER+: Incremental Multi-View Output-Specialization for Streaming Multi-Label Learning
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DOI：10.1016/j.inffus.2026.104283.png)
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Abstract
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• Proposes iHOMER+, a multi-view ensemble for drifting multi-label streams • Reviews local (problem transformation) and global (algorithm adaptation) strategies • Introduces output-specialization methods that cluster labels for hybrid modeling • Models correlations, across label, instance, and feature spaces • Combines three concurrent learners for robust drift adaptation
Keywords:
multi-label classification
incremental decision tree
data stream mining
concept drift
online learning
ensemble
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