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Label Ranking Forests
Author(s) -
Sá Cláudio Rebelo,
Soares Carlos,
Knobbe Arno,
Cortez Paulo
Publication year - 2017
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12166
Subject(s) - ranking (information retrieval) , computer science , random forest , decision tree , machine learning , artificial intelligence , set (abstract data type) , ensemble learning , tree (set theory) , object (grammar) , ranking svm , data mining , training set , pattern recognition (psychology) , mathematics , mathematical analysis , programming language
The problem of Label Ranking is receiving increasing attention from several research communities. The algorithms that have been developed/adapted to treat rankings of a fixed set of labels as the target object, including several different types of decision trees (DT). One DT‐based algorithm, which has been very successful in other tasks but which has not been adapted for label ranking is the Random Forests (RF) algorithm. RFs are an ensemble learning method that combines different trees obtained using different randomization techniques. In this work, we propose an ensemble of decision trees for Label Ranking, based on Random Forests, which we refer to as Label Ranking Forests (LRF). Two different algorithms that learn DT for label ranking are used to obtain the trees. We then compare and discuss the results of LRF with standalone decision tree approaches. The results indicate that the method is highly competitive.