Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99141
Title: Transductive ordinal regression
Authors: Seah, Chun-Wei
Tsang, Ivor Wai-Hung
Ong, Yew Soon
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Seah, C., Tsang, I. W., & Ong, Y. (2012). Transductive ordinal regression. IEEE transactions on neural networks and learning systems, 23(7), 1074-1086.
Series/Report no.: IEEE transactions on neural networks and learning systems
Abstract: Ordinal regression is commonly formulated as a multiclass problem with ordinal constraints. The challenge of designing accurate classifiers for ordinal regression generally increases with the number of classes involved, due to the large number of labeled patterns that are needed. The availability of ordinal class labels, however, is often costly to calibrate or difficult to obtain. Unlabeled patterns, on the other hand, often exist in much greater abundance and are freely available. To take benefits from the abundance of unlabeled patterns, we present a novel transductive learning paradigm for ordinal regression in this paper, namely transductive ordinal regression (TOR). The key challenge of this paper lies in the precise estimation of both the ordinal class label of the unlabeled data and the decision functions of the ordinal classes, simultaneously. The core elements of the proposed TOR include an objective function that caters to several commonly used loss functions casted in transductive settings, for general ordinal regression. A label swapping scheme that facilitates a strictly monotonic decrease in the objective function value is also introduced. Extensive numerical studies on commonly used benchmark datasets including the real-world sentiment prediction problem are then presented to showcase the characteristics and efficacies of the proposed TOR. Further, comparisons to recent state-of-the-art ordinal regression methods demonstrate the introduced transductive learning paradigm for ordinal regression led to the robust and improved performance.
URI: https://hdl.handle.net/10356/99141
http://hdl.handle.net/10220/13515
ISSN: 2162-237X
DOI: http://dx.doi.org/10.1109/TNNLS.2012.2198240
Rights: © 2012 IEEE
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

Google ScholarTM

Check

Altmetric

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.