Multi-dimensional classification (MDC) aims to build classification models for multiple heteroge-nous class spaces simultaneously, where each class space characterizes the semantics of an object w.r.t. onespecific dimension. Modeling dependencies among class spaces plays a key role in solving MDC tasks, wheremost approaches work by assuming directed acyclic graph (DAG) structure or random chaining structureover class spaces. Different from existing probabilistic strategies, a deterministic strategy namedSeemfordependency modeling is proposed in this paper via stacked dependency exploitation. In the first-level, pair-wise dependencies are considered which can be modeled more reliably than modeling full dependencies amongall class spaces by DAG or chaining structure. In the second-level, the class label of unseen instance w.r.t.each class space is determined by adaptively stacking predictive outputs from first-level pairwise classifiers.Experimental results show that stacked dependency exploitation leads to superior performance against state-of-the-art MDC approaches.