A Simple Approach to Ordinal Classification

By Eibe Frank, Mark A. Hall. European Conference on Machine Learning, 2001

683 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

View Paper: https://www.semanticscholar.org/paper/76fc26939d73c565431e1f38107a4245e739284e

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Citations in the corpus

Ordinal Regression with Multiple Output CNN for Age Estimation (2016, methodology): “On the other hand, to directly utilize the well-studied classiﬁcation algorithms, the ordinal regression problem is transferred as a series of simpler binary classiﬁcation sub-problems [10][21].”
Ordinal Regression by Extended Binary Classification (2006, background): “Frank and Hall [6] proposed to solve each binary classification problem independently and combine the binary outputs to a rank.”
New approaches to support vector ordinal regression (2005, neutral): “Frank and Hall (2001) converted an ordinal regression problem into nested binary classification problems that encode the ordering of the original ranks and then organized the results of these binary classifiers in some ad hoc way for prediction.”
Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews (2002, methodology): “I plan to experiment with ordinal classification of reviews in the five star rating system, using the algorithm of Frank and Hall (2001).”
Support Vector Ordinal Regression (2007, neutral): “Frank and Hall (2001) converted an ordinal regression problem into nested binary classification problems that encode the ordering of the original ranks and then organized the results of these binary classifiers in some ad hoc way for prediction.”
Decision trees: a recent overview (2011, background): “Another approach that enables decision tree algorithms to make use of ordering information in ordinal class attributes is presented in Frank and Hall (2001).”
Learning From Crowds (2010, methodology): “A simple approach is to convert the ordinal data into a series of binary data (Frank and Hall, 2001).”
Ordinal Regression Methods: Survey and Experimental Study (2016, neutral): “They have been adapted to ordinal regression [73], and one of the proposed ordinal ELMs also considers OrderedPartitions targets.”
Fact Checking: Task definition and dataset construction (2014, background): “Therefore it is better to consider fact-checking as an ordinal classification task (Frank and Hall, 2001), thus allowing systems to capture the nuances of the task.”
Supervised learning from multiple experts: whom to trust when everyone lies a bit (2009, methodology): “Note that there is an ordering in the labels 1 ... K.A simple approach is to convert the ordinal data into a series of binary data (Frank & Hall, 2001).”
Graph-based approach for airborne light detection and ranging segmentation (2014, methodology): “OrdinalClassClassifier w is an ensemble method designed for ordinal classification problems (Frank and Hall, 2001) with J48 base classifiers, confidence threshold C=0.”
Rule learning with monotonicity constraints (2009, methodology): “Therefore, we decided to improve its performance and use it in the ordinal setting by combining it with a simple approach to ordinal classi.cation proposed by (Frank & Hall, 2001).”
Ordinal hyperplanes ranker with cost sensitivities for age estimation (2011, background): “Although not a hyperplane-based ranker, an early approach [7] also integrates K classification probabilities according to the orders for ranking.”
Deep Ordinal Regression Network for Monocular Depth Estimation (2018, methodology): “For instance, Frank and Hall [15] applied some decision trees as binary classiﬁers for ordinal regression.”
Label ranking by learning pairwise preferences (2008, methodology): “Frank and Hall (2001) suggest a method for translating an ordinal regression problem into a set of ordinary (binary) classification problems.”