A robust method for linear regression of symbolic interval data

• Published in: Pattern recognition letters Vol. 31; no. 13; pp. 1991 - 1996
• Main Authors: Domingues, Marco A.O., de Souza, Renata M.C.R., Cysneiros, Francisco José A.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2010
• Subjects: Outliers; Symbolic data analysis; Symbolic interval-valued data; Symmetrical linear regression; Symmetrical linear regression; Outliers; Symbolic data analysis; Symbolic interval-valued data
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2010.06.008

This paper introduces a new linear regression method for interval valued-data. The method is based on the symmetrical linear regression methodology such that the prediction of the lower and upper bounds of the interval value of the dependent variable is not damaged by the presence of interval-valued data outliers. The method considers mid-points and ranges of the interval values assumed by the variables in the learning set. The prediction of the boundaries of an interval is accomplished through a combination of predictions from mid-point and range of the interval values. The evaluation of the method is based on the average behavior of a pooled root mean-square error. Experiments with real and simulated symbolic interval data sets demonstrate the usefulness of this symbolic symmetrical linear regression method.