Multi-Label Rule Learning
This project aimed at solving multi-label classification problems using rule learning algorithms. Multi-label classification has received a lot of attention in the recent machine learning literature and is nowadays used in applications as diverse as music categorization, semantic scene classification, or protein function classification. Multi-label classification becomes particularly challenging when it comes to discovering hidden dependencies between labels. As rules provide a natural form of expressing such dependencies, they are a natural choice to capture label correlations. The Lichtenberg cluster was used to evaluate the predictive accuracy of the developed algorithms and existing baselines using synthetic and realworld benchmark data sets.
We utilized the well-known gradient boosting framework to develop a novel rule learning algorithm that is able to optimize different types of multivariate loss functions (decomposable or non-decomposable) and therefore can flexibly be tailored to different multi-label evaluation measures that are commonly used to assess the quality of predictions in multi-label classification.
As shown by the experiments that have been conducted on the Lichtenberg cluster, our rule learning method can successfully be used to optimize decomposable and non-decomposable loss functions. It has further been shown that it is able to outperform conventional state-of-theart boosting methods on data sets of moderate size.
Existing boosting-based approaches to multi-label classification are usually limited to the use of decomposable loss function, which allows for a straight-forward adaption of gradient boosting from binary to multi-label classification. However, capturing dependencies between labels has become a key motivation for research in this machine learning domain. To address such use cases, we proposed a generalization of the gradient boosting framework that supports the minimization of non-decomposable loss function. In the future, it could could serve as a basis for developing new methods specifically tailored to this particular type of loss functions. As an instantiation of the framework, we presented a novel rule learning algorithm that has been shown to achieve state-of-the-art performance. A drawback of the proposed algorithm are the computational costs that come with the use of large data sets with many labels. To compensate for this, we plan to investigate approximations that exploit the sparsity in the label space in future work.