Interval symbolic data is a complex data type that can often be obtained by summarizing
large datasets. All existing linear regression approaches for interval data use certain fixed
reference points to model intervals, such as midpoints, ranges and lower and upper bounds.
This is a limitation, because different datasets might be better represented by different
reference points. In this paper, we propose a new method for extracting knowledge from
interval data. Our parametrized approach automatically extracts the best reference points …

Souza et al.(Knowledge-Based Systems, 131 (2017), pp. 149-159) pointed out that although
several approaches have been proposed in the literature for fitting interval linear regression
models (linear regression models its parameters are represented as intervals). However, as
there are flaws in all the existing approaches, it is scientifically incorrect to use these
approaches in real life problems. To resolve the flaws of the existing approaches, Souza et
al. proposed a new approach for fitting interval linear regression models. After a deep study …