Bilkent University
Department of Computer Engineering
CS 590/690 SEMINAR
Diversity-Guided Dynamic Ensemble Selection for Multi-Label Data Streams
Mert Barkın Er
Master Student
(Supervisor:Prof.Dr.Fazlı Can)
Computer Engineering Department
Bilkent University
Abstract: In multi-label data stream classification, concept drift can change both feature distributions and label dependencies over time. Existing ensemble methods either rely on fixed accuracy–diversity trade-offs or use homogeneous base learner pools. We propose ML-DynED, an ensemble method that maintains a heterogeneous pool of Multi-Label Hoeffding Trees, Multi-Label k-Nearest Neighbors, and Binary Relevance classifiers, and selects an active sub-ensemble using a multi-label adaptation of Maximal Marginal Relevance. In experiments on 30 real-world and synthetic datasets, ML-DynED achieves the best micro-F1 on several benchmarks, with an average rank of 13.29 across 32 methods. Although it does not consistently outperform the strongest instance-based baselines, the results suggest that diversity-aware selection is a competitive alternative to homogeneous ensemble design for multi-label data streams.
DATE: March 23, Monday @ 15:30 Place: EA 502