Multi-dimensional classification via stacked dependency exploitation

Abstract

Multi-dimensional classification (MDC) aims to build classification models for multiple heterogenous class spaces simultaneously, where each class space characterizes the semantics of an object w.r.t. one specific dimension. Modeling dependencies among class spaces plays a key role in solving MDC tasks, where most approaches work by assuming directed acyclic graph (DAG) structure or random chaining structure over class spaces. Different from existing probabilistic strategies, a deterministic strategy named Seem for dependency modeling is proposed in this paper via stacked dependency exploitation. In the first-level, pairwise dependencies are considered which can be modeled more reliably than modeling full dependencies among all 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 stateof-the-art MDC approaches.

Multi-target prediction for dummies using two-branch neural networks

“…MTP problem settings like multi-dimensional classification could be tackled using the same configuration for the output layer. Also, similarly to the work by Jia and Zhang (2020), comparisons can be made using modified multivariate regression datasets and baseline methods that are used in multi-label classification (Binary Relevance, Classifier chains, Label Powerset). Datasets that combine heterogeneous targets (for example, binary, multi-class, and real-valued simultaneously) are not suitable for our architecture as the use of a single loss function limits us to multi-task learning problems with homogeneous targets.…”

Multi-dimensional classification: paradigm, algorithms and beyond

“…The idea of super-class partition can mitigate the huge class combinations problem in CP and then class combinations not appearing in training set might also be returned by SC. It inspires the pairwise grouping operation in following works to consider the dependencies between two dimensions [56,67,71,74]. SC has been usually used as a compared algorithm in MDC researches and the corresponding paper has received 65 citations according to Google Scholar statistics (by February 2024).…”

“…The basic idea of stacked dependency exploitation for MDC (SEEM) is to consider the class dependencies in a two-level manner, where the dependencies between each pair of dimensions and the dependencies between each dimension and the remaining dimensions are considered in the first level and second level, respectively [71].…”