Tackling Class Imbalance with Ranking

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Cruz, R (Author) Other; Fernandes, K (Author) Other; Jaime S Cardoso (Author) FEUP; Da Costa, JP (Author) FCUP

Conference proceedings International

Title: 2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)

Pages: 2182-2187

International Joint Conference on Neural Networks (IJCNN)

Vancouver, CANADA, JUL 24-29, 2016

Indexing

ISI Web of Knowledge - 24 Citations

Scopus - 31 Citations

Scientific classification

FOS: Natural sciences > Mathematics

Other information

Authenticus ID: P-00M-8F6

DOI: 10.1109/ijcnn.2016.7727469

Abstract (EN): In classification, when there is a disproportion in the number of observations in each class, the data is said to be class imbalance. Class imbalance is pervasive in real world applications of data classification and has been the focus of much research. The minority class contributes too little to the decision boundary because the learning process learns from each observation in isolation. In this paper, we discuss the application of learning pairwise rankers as a solution to class imbalance. We compare ranking models to alternatives from the literature.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6