Case-Based Label Ranking
Case-Based Label Ranking
复制标题
基于案例的标签排名
DOI：
10.1007/11871842_53
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
Eyke Hüllermeier
中科院分区：
文献类型：
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作者：
K. Brinker;Eyke Hüllermeier
标签排名研究学习从实例到预定义标签集上的排名的映射问题。我们从基于案例的角度来处理这种设置，并提出了一个复杂的k-NN框架作为替代以前的二进制分解技术。它具有吸引人的属性的透明度，并基于聚合模型，允许一个将各种成对的损失函数的标签排名。除了这些概念上的优势，我们的经验表明，我们的基于案例的方法是有竞争力的国家的最先进的基于模型的学习者的准确性，而计算效率更高。此外，我们的方法提出了一种自然的方式将置信度分数与预测相关联，这是以前的方法所不具备的属性。
Label ranking studies the problem of learning a mapping from instances to rankings over a predefined set of labels. We approach this setting from a case-based perspective and propose a sophisticated k-NN framework as an alternative to previous binary decomposition techniques. It exhibits the appealing property of transparency and is based on an aggregation model which allows one to incorporate a variety of pairwise loss functions on label rankings. In addition to these conceptual advantages, we empirically show that our case-based approach is competitive to state-of-the-art model-based learners with respect to accuracy while being computationally much more efficient. Moreover, our approach suggests a natural way to associate confidence scores with predictions, a property not being shared by previous methods.