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A new method to fit a linear regression model for interval-valued data. (English) Zbl 1132.68617

Biundo, Susanne (ed.) et al., KI 2004: Advances in artificial intelligence. 27th annual German conference in AI, KI 2004, Ulm, Germany, September 20–24, 2004, Proceedings. Berlin: Springer (ISBN 978-3-540-23166-0/pbk). Lecture Notes in Computer Science 3238. Lecture Notes in Artificial Intelligence, 295-306 (2004).
Summary: This paper introduces a new approach to fit a linear regression model on interval-valued data. Each example of the learning set is described by a feature vector where each feature value is an interval. In the proposed approach, it is fitted two linear regression models, respectively, on the mid-point and range of the interval values assumed by the variables on the learning set. The prediction of the lower and upper bound of the interval value of the dependent variable is accomplished from its mid-point and range which are estimated from the fitted linear regression models applied to the mid-point and range of each interval values of the independent variables. The evaluation of the proposed prediction method is based on the estimation of the average behaviour of root mean squared error and of the determination coefficient in the framework of a Monte Carlo experience in comparison with the method proposed by Billard and Diday.
For the entire collection see [Zbl 1131.68004].

MSC:

68T05 Learning and adaptive systems in artificial intelligence
62-07 Data analysis (statistics) (MSC2010)
62J05 Linear regression; mixed models

Software:

GOLOG
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