Title Representing ordinal input variables in the context of ordinal classification
Authors Gutiérrez P.A. , PÉREZ ORTIZ, MARÍA, SÁNCHEZ MONEDERO, JAVIER, Hervás-Martínez C.
External publication No
Means 2016 International Joint Conference On Neural Networks (ijcnn)
Scope Conference Paper
Nature Científica
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007188815&doi=10.1109%2fIJCNN.2016.7727468&partnerID=40&md5=9428ea984fd40e9a41529ee2f8369ef8
Publication date 01/01/2016
Scopus Id 2-s2.0-85007188815
DOI 10.1109/IJCNN.2016.7727468
Abstract Ordinal input variables are common in many supervised and unsupervised machine learning problems. We focus on ordinal classification problems, where the target variable is also categorical and ordinal. In order to represent categorical input variables for measuring distances or applying continuous mapping functions, they have to be transformed to numeric values. This paper evaluates five different methods to do so. Two of them are commonly applied by practitioners, the first one based on binarising the ordinal input variable using standard indicator variables (NomBin), and the second one based on directly mapping each category to a consecutive natural number (Num). Furthermore, three novel proposals are evaluated in this paper: 1) an ordinal binarisation based on considering the order of the input variable (OrdBin), 2) the analysis of pairwise distances between input patterns to recover the latent variable generating the ordinal one (NumLVR), and 3) the refinement of the standard numeric transformation by recovering the distance between sets of patterns of consecutive categories (NumCDR). A thorough empirical evaluation is made, considering 12 datasets, 5 performance metrics and 4 classifiers (2 of them of nominal nature and 2 of ordinal nature). The results show that the Nom-Bin representation method leads to the worst results, and that both Num and NumCDR methods obtain very good performance, although NumCDR results are consistently better for almost all performance metrics and classifiers considered. © 2016 IEEE.
Keywords Bins; Classifiers; Learning systems; Mapping; Feature representation; Feature transformations; Input features; Ordinal classification; Ordinal regression; Classification (of information)
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