Multilabel Rule Learning

归纳规则学习是机器学习中一个非常传统、成熟的研究领域。当一个人不仅对准确的预测感兴趣，而且还需要一个可以被领域专家理解、分析和定性评估的可解释理论时，通常会使用规则学习算法。理想情况下，通过揭示数据中隐式捕获的模式和规则，基于规则的理论可以在应用程序领域产生新的见解。另一方面，在许多机器学习任务中，同时寻求多个目标变量的预测，这个问题被称为多目标预测。多标签分类是一种重要的特殊情况，其中所有输出变量都是二进制的。该领域最先进的方法能够通过考虑这些输出变量之间的依赖关系来提高性能。然而，在学习这些依赖关系的显式表示方面只做了很少的工作，这本身就是一项有价值的数据挖掘任务。我们相信，对这一问题以规则为基础的观点将大大增进我们的理解并导致更好的实际解决办法。因此，该项目的主要目标是将多标签分类和归纳规则学习的研究联系起来，并开发用于多标签分类的可扩展规则学习算法。在机器学习的两个研究领域的交叉点工作，我们将为这两个领域做出贡献。因此，在一个非常高的层面上，这个项目的目标是(i)开发一个统一的框架来表示不同类型的标签依赖关系，并分析其对多标签分类问题的表达能力，（ii）面对从数据中学习多标签规则集的算法挑战，以及（iii）与最先进的系统相比，评估这些规则的预测和描述性能。

Inductive rule learning is a very traditional, well-established resaerch area in machine learning. Rule learning algorithms are typically employed when one is not only interested in accurate predictions but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing the patterns and regularities that are implicitly captured in the data, a rule-based theory yields new insights in the application domain. On the other hand, in many machine learning tasks, predictions are sought for multiple target variables simultaneously, a problem known as multi-target prediction. Multilabel classification, where all output variables are binary, is an important special case. State-of-the-art methods in this area are able to improve performance by taking dependencies between these output variables into account. However, only little work has been done in learning explicit representations of such dependencies, which is a worth-while data mining task in itself.We are convinced that a rule-based view of this problem will greatly enhance our understanding and lead to better practical solutions. The main goal of this project is thus to connect research in multilabel classification and inductive rule learning, and to develop scalable rule learning algorithms for multilabel classification. Working in the intersection of two research areas in machine learning, we will make contributions to bothfields. At a very high level, the objectives of this project are thus (i) to develop a unified framework for representing different types of label dependencies, and analyze its expressive power for multilabel classification problems, (ii) to face the algorithmic challenges of learning multilabel rule sets from data, and (iii) to evaluate the predictive and descriptive performance of such rules in comparison to state-of-the-art systems.