Efficient decomposition-based multiclass and multilabel classification

Decomposition-based methods are widely used for multiclass and multilabel classification. These approaches transform or reduce the original task to a set of smaller possibly simpler problems and allow thereby often to utilize many established learning algorithms, which are not amenable to the original task. Even for directly applicable learning algorithms, the combination with a decomposition-scheme may outperform the direct approach, e.g., if the resulting subproblems are simpler (in the sense of learnability). This thesis addresses mainly the efficiency of decomposition-based methods and provides several contributions improving the scalability with respect to the number of classes or labels, number of classifiers and number of instances. Initially, we present two approaches improving the efficiency of the training phase of multiclass classification. The first of them shows that by minimizing redundant learning processes, which can occur in decomposition-based approaches for multiclass problems, the number of operations in the training phase can be significantly reduced. The second approach is tailored to Näıve Bayes as base learner. By a tight coupling of Näıve Bayes and arbitrary decompositions, it allows an even higher reduction of the training complexity with respect to the number of classifiers. Moreover, an approach improving the efficiency of the testing phase is also presented. It is capable of reducing testing effort with respect to the number of classes independently of the base learner. Furthermore, efficient decomposition-based methods for multilabel classification are also addressed in this thesis. Besides proposing an efficient prediction method, an approach rebalancing predictive performance, time and memory complexity is presented. Aside from the efficiency-focused methods, this thesis contains also a study about a special case of the multilabel classification setting, which is elaborated, formalized and tackled by a prototypical decomposition-based approach.