Label Ranking Forests

• Type: Article in International Scientific Journal
• Date: 2017

Authors

de Sa, CR (Author) Other; Carlos Soares (Author) FEUP; Knobbe, A (Author) Other; Cortez, P (Author) Other

Journal

Title: Expert Systems

Vol. 34

Final page: e12166

ISSN: 0266-4720

Publisher: Wiley-Blackwell

Indexing

ISI Web of Knowledge - 30 Citations

Scopus - 31 Citations

Other information

Authenticus ID: P-00M-FN6

DOI: 10.1111/exsy.12166

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8