Distance-Based Decision Tree Algorithms for Label Ranking

• Type: Article in International Conference Proceedings Book
• Date: 2015

Authors

de Sa, CR (Author) Other; Rebelo, C (Author) Other; Carlos Soares (Author) FEUP; Knobbe, A (Author) Other

Conference proceedings International

Title: PROGRESS IN ARTIFICIAL INTELLIGENCE

Pages: 525-534

17th Portuguese Conference on Artificial Intelligence (EPIA)

Univ Coimbra, Coimbra, PORTUGAL, SEP 08-11, 2015

Indexing

ISI Web of Knowledge - 6 Citations

Scopus - 8 Citations

Scientific classification

CORDIS: Physical sciences > Computer science > Cybernetics > Artificial intelligence

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-00G-SXT

DOI: 10.1007/978-3-319-23485-4_52

Abstract (EN): The problem of Label Ranking is receiving increasing attention from several research communities. The algorithms that have developed/adapted to treat rankings as the target object follow two different approaches: distribution-based (e.g., using Mallows model) or correlation-based (e.g., using Spearman's rank correlation coefficient). Decision trees have been adapted for label ranking following both approaches. In this paper we evaluate an existing correlation-based approach and propose a new one, Entropy-based Ranking trees. We then compare and discuss the results with a distribution-based approach. The results clearly indicate that both approaches are competitive.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10