Ordinal Regression by Extended Binary Classification

By Ling Li, Hsuan-Tien Lin. Neural Information Processing Systems, 2006

315 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/774f3f2569e6fe566943da726821b87873ead10b

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Citations in the corpus

Ordinal hyperplanes ranker with cost sensitivities for age estimation (2011, methodology): “Among the above approaches, RankBoost, RED-SVM and MHR are rankingbased, and the others are classification- or regression-based approaches.”
Information, Divergence and Risk for Binary Experiments (2009, background): “In particular, a generalised form of Taylor’s theorem and Jensen’s inequality underpin many of the results in this paper.”
Incremental Support Vector Learning for Ordinal Regression (2015, methodology): “To sum up, there are two kinds of approaches to keep such ordinal thresholds, i.e., the explicit approach [6], [8], and the implicit approach [8], [10], [15], [17], [20], [23].”
Rank consistent ordinal regression for neural networks with application to age estimation (2019, background): “Let C be a K×K cost matrix, where Cy,rk is the cost of predicting an example (x, y) as rank rk (Li and Lin, 2007).”
Ordinal Regression Methods: Survey and Experimental Study (2016, unknown): “A reduction framework can be found in the works of Lin and Li [41], [104], where ordinal regression is reduced to binary classification by applying three steps:”
Leveraging molecular structure and bioactivity with chemical language models for de novo drug design (2023, methodology): “2c) and (ii) there is a natural ordering of the PI3K γ ligands according to their pIC 50 values, the bioactivity prediction task was framed as an ordinal classi ﬁ cation task, i.e., classi ﬁ cation with a class order 39 .”
On the Consistency of Ordinal Regression Methods (2014, background): “is has been formalized as the V-shape property [12].”
Ordinal Regression with Multiple Output CNN for Age Estimation (2016, methodology): “Recently, to directly utilize the well-studied classiﬁcation algorithms, the ordinal regression problem is transformed into a series of simpler binary classiﬁcation sub-problems [10][21].”