Less Is More: Principled Diversity in Heterogeneous Anomaly Detection Ensembles

Heterogeneous anomaly detection ensembles improve robustness by combining complementary detectors, yet existing approaches often rely on heuristic detector selection, fixed contamination assumptions, and equal weighting. We investigate whether compact ensembles of complementary detectors can outperform substantially larger heterogeneous configurations through diversity-aware weighting and adaptive contamination estimation. Experiments on 22 benchmark datasets show that a compact ensemble of four complementary classical detectors outperforms an eleven-detector ensemble containing deep learning components, while requiring only 13.8% of the computational cost. Across the benchmark, the proposed ensemble variants achieve strong rankings while remaining competitive with the strongest individual detectors (Friedman χ2=71.58, p<0.001). These findings suggest that detector diversity, rather than ensemble size or architectural complexity, is the primary driver of robust unsupervised anomaly detection performance in resource-constrained environments.