Symbolic clustering using a new dissimilarity measure
Pattern Recognition
A monothetic clustering method
Pattern Recognition Letters
Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data
Clustering of interval data based on city-block distances
Pattern Recognition Letters
Adaptive Hausdorff distances and dynamic clustering of symbolic interval data
Pattern Recognition Letters
Forecasting models for interval-valued time series
Neurocomputing
Fitting a Least Absolute Deviation Regression Model on Interval-Valued Data
SBIA '08 Proceedings of the 19th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Detection of chain structures embedded in multidimensional symbolic data
Pattern Recognition Letters
Constrained linear regression models for symbolic interval-valued variables
Computational Statistics & Data Analysis
Bivariate generalized linear model for interval-valued variables
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A robust method for linear regression of symbolic interval data
Pattern Recognition Letters
Estimation of a flexible simple linear model for interval data based on set arithmetic
Computational Statistics & Data Analysis
Information Sciences: an International Journal
A resampling approach for interval-valued data regression
Statistical Analysis and Data Mining
Likelihood-based Imprecise Regression
International Journal of Approximate Reasoning
Robust regression with application to symbolic interval data
Engineering Applications of Artificial Intelligence
Semidefinite Programming-Based Method for Implementing Linear Fitting to Interval-Valued Data
International Journal of Fuzzy System Applications
Neurocomputing
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This paper introduces a new approach to fitting a linear regression model to symbolic interval data. Each example of the learning set is described by a feature vector, for which each feature value is an interval. The new method fits a linear regression model on the mid-points and ranges of the interval values assumed by the variables in the learning set. The prediction of the lower and upper bounds of the interval value of the dependent variable is accomplished from its mid-point and range, which are estimated from the fitted linear regression model applied to the mid-point and range of each interval value of the independent variables. The assessment of the proposed prediction method is based on the estimation of the average behaviour of both the root mean square error and the square of the correlation coefficient in the framework of a Monte Carlo experiment. Finally, the approaches presented in this paper are applied to a real data set and their performance is compared.