TY - GEN
T1 - Mining association rules for label ranking
AU - De Sá, Cláudio Rebelo
AU - Soares, Carlos
AU - Jorge, Alípio Mário
AU - Azevedo, Paulo
AU - Costa, Joaquim
N1 - Conference code: 15
PY - 2011/6/8
Y1 - 2011/6/8
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=79957929921&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-20847-8-36
DO - 10.1007/978-3-642-20847-8-36
M3 - Conference contribution
AN - SCOPUS:79957929921
SN - 978-3-642-20846-1
T3 - Lecture Notes in Computer Science
SP - 432
EP - 443
BT - Advances in Knowledge Discovery and Data Mining
A2 - Huang, Joshua Zhexue
A2 - Cao, Longbing
A2 - Srivastava, Jaideep
PB - Springer
CY - Berlin, Heidelberg
T2 - 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011
Y2 - 24 May 2011 through 27 May 2011
ER -