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Mining Association Rules for Label Ranking

2011; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-642-20847-8_36

1611-3349

Cláudio Rebelo de Sá, Carlos Soares, Alí­pio Jorge, Paulo J. Azevedo, Joaquim Pinto da Costa,

Machine Learning and Data Classification

Recently, a number of learning algorithms have been adapted for label ranking, including instance-based and tree-based methods. In this paper, we propose an adaptation of association rules for label ranking. The adaptation, which is illustrated in this work with APRIORI Algorithm, essentially consists of using variations of the support and confidence measures based on ranking similarity functions that are suitable for label ranking. We also adapt the method to make a prediction from the possibly conflicting consequents of the rules that apply to an example. Despite having made our adaptation from a very simple variant of association rules for classification, the results clearly show that the method is making valid predictions. Additionally, they show that it competes well with state-of-the-art label ranking algorithms.