On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics
On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics
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DOI：
10.1007/978-3-030-33778-0_9
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发表时间：
2019-08
期刊：
影响因子：
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通讯作者：
Michael Rapp;E. Mencía;Johannes Fürnkranz
中科院分区：
文献类型：
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作者：
Michael Rapp;E. Mencía;Johannes Fürnkranz
最近，一些作者提倡使用规则学习算法来建模多标签数据，因为规则是可解释的，并且可以由领域专家理解，分析或定性评估。许多规则学习算法采用启发式引导的规则搜索，这些规则对训练数据中包含的启发式进行建模，并且普遍认为启发式的选择对学习器的预测性能具有显着影响。虽然规则学习算法的性质已经在单标签分类领域进行了研究，但还没有考虑到多标签分类的特殊性的工作。这是令人惊讶的，因为多标签预测的质量通常是根据各种不同的、可能相互竞争的性能指标来评估的，这些指标不能同时由单个学习者进行优化。在这项工作中，我们的经验表明，这是至关重要的权衡不同的规则的一致性和覆盖范围，这取决于多标签措施应通过模型进行优化。基于这些发现，我们强调需要可配置的学习者，可以灵活地使用不同的算法。正如我们的实验所揭示的那样，启发式的选择并不直接，因为搜索局部优化度量的规则通常不会导致全局最大化该度量的模型。
Recently, several authors have advocated the use of rule learning algorithms to model multi-label data, as rules are interpretable and can be comprehended, analyzed, or qualitatively evaluated by domain experts. Many rule learning algorithms employ a heuristic-guided search for rules that model regularities contained in the training data and it is commonly accepted that the choice of the heuristic has a significant impact on the predictive performance of the learner. Whereas the properties of rule learning heuristics have been studied in the realm of single-label classification, there is no such work taking into account the particularities of multi-label classification. This is surprising, as the quality of multi-label predictions is usually assessed in terms of a variety of different, potentially competing, performance measures that cannot all be optimized by a single learner at the same time. In this work, we show empirically that it is crucial to trade off the consistency and coverage of rules differently, depending on which multi-label measure should be optimized by a model. Based on these findings, we emphasize the need for configurable learners that can flexibly use different heuristics. As our experiments reveal, the choice of the heuristic is not straight-forward, because a search for rules that optimize a measure locally does usually not result in a model that maximizes that measure globally.