Appliance recognition in non-intrusive load monitoring (NILM) is typically framed as either a single-label or multi-label classification problem. Single-label approaches predict one appliance per activation event, while multi-label approaches predict the set of simultaneously active appliances. Both paradigms have complementary strengths: single-label methods excel at isolating dominant loads with clear event signatures, whereas multi-label methods handle concurrent appliance operation. This paper proposes a combined approach that leverages both paradigms within a unified recognition pipeline. We train a single-label classifier for isolated events and a multi-label classifier for overlapping load periods, then fuse their outputs via a learned gating mechanism. Evaluation on the UK-DALE dataset shows that the combined approach outperforms either method in isolation, achieving higher F1 scores across all evaluated appliances.