Instance Ranking with Multiple Linear Regression: Pointwise vs. Listwise Approaches

• Type: Article in International Conference Proceedings Book
• Date: 2014

Authors

João Brito (Author) FEUP; João Mendes-Moreira (Author) FEUP

Conference proceedings International

Title: PROCEEDINGS OF THE 2014 9TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI 2014)

Pages: 1040-1045

9th Iberian Conference on Information Systems and Technologies (CISTI)

Barcelona, SPAIN, JUN 18-21, 2014

Indexing

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Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

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

Other information

Authenticus ID: P-00A-3X7

DOI: 10.1109/cisti.2014.6877057

Abstract (EN): This paper presents a comparison between listwise and pointwise approaches for instance ranking using Multiple Linear Models. A theoretical review of both approaches is performed, including the evaluation methods. Experiments done in seven datasets from 4 different problems show that the pointwise approach is slightly better or similar than the listwise approach. However the models obtained with the listwise approach are more interpretable because they have in average fewer features than the models obtained with the pointwise approach. The obtained results are important for problems where interpretable ranking models are necessary.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6