Decomposition-Based Classifier Chains for Multi-Dimensional Classification

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In multi-dimensional classification, the semantics of objects are characterized by multiple class variables from different dimensions. To model the dependencies among class variables, one natural strategy is to build a number of multiclass classifiers in a chaining structure, one per dimension, where the subsequent classifiers on the chain augment the feature space with all labeling information used by the preceding classifiers. However, it is shown that this strategy cannot compete with existing state-of-the-art approaches via comparative studies. One possible reason is that inaccurate predictions of preceding classifiers would degenerate the performance of subsequent ones. Besides, it is more difficult to learn a multiclass classifier than a binary one with the same accuracy, and better performance can be expected if the multi-dimensional classification problem can be solved by building multiple binary classifiers in a chaining structure. Based on these conjectures, this article proposes an approach, which builds a chain of binary classifiers to solve the multi-dimensional classification problem with the help of one-versus-one decomposition. To address the issue that different one-versus-one decomposed problems involve different training examples, the feature space is augmented with the binary predictions of preceding classifiers on the chain to train the subsequent ones. To alleviate the effect of the specified chaining order, the ensemble version of the proposed approach is further investigated. Comparative studies over 20 benchmark datasets clearly show the superiority of the proposed approach against the state-of-the-art multi-dimensional classification baselines.