Mining Association Rules for Label Ranking

• Type: Article in International Conference Proceedings Book
• Date: 2011

Authors

Claudio Rebelo de Sa (Author) Other; Carlos Soares (Author) FEP; Alipio Mario Jorge (Author) FCUP; Paulo Azevedo (Author) Other; Joaquim Costa (Author) FCUP

Conference proceedings International

Title: ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PT II: 15TH PACIFIC-ASIA CONFERENCE, PAKDD 2011

Pages: 432-443

15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

Shenzhen, PEOPLES R CHINA, MAY 24-27, 2011

Indexing

ISI Web of Knowledge - 29 Citations

Scopus - 34 Citations

Scientific classification

CORDIS: Physical sciences > Computer science

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-002-VX3

DOI: 10.1007/978-3-642-20847-8-36

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12