Eufrasio de Andrade Lima Neto

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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.(More)
This paper introduces an approach to fitting a constrained linear regression model to interval-valued data. Each example of the learning set is described by a feature vector for which each feature value is an interval. The new approach fits a constrained linear regression model on the midpoints and range of the interval values assumed by the variables in(More)
Current symbolic regression methods visualize problems from an optimization point of view and do not consider the probabilistic aspects related to regression models. In this paper, we present the bivariate generalized linear model (BGLM) proposed by Iwasaki and Tsubaki [5] in the context of interval-valued data sets. Important aspects related to the BGLM(More)
Interval-valued data arise in practical situations such as recording monthly interval temperatures at meteorological stations, daily interval stock prices, etc. This paper introduces a multinomial logistic regression method for interval-valued data in order to classify items described by interval-valued variables into a pre-defined number of a priori(More)
This paper introduces some approaches to fitting a constrained linear regression model to interval-valued data. The use of inequality constraints guarantee mathematical coherence between the predicted values of the lower bound (ŷLi) and the upper bound (ŷUi). The authors also propose expressions to the goodness of fit measure called determination(More)
This paper introduces some approaches to fitting a constrained linear regression model to interval-valued data. The new methods show the importance of the range's information in their prediction performance and the use of inequality constraints to guarantee mathematical coherence between the predicted values of the lower bound (gamma<sub>lflooriexcl</sub>)(More)