Abstract
This contribution describes an EM-like piecewise linear regression algorithm that uses information about the target variable to determine a meaningful partitioning of the input space. The main goal of this approach is to incorporate information about the target variable in the prototype selection process of a piecewise regression approach. Furthermore, the proposed approach is designed to provide an interpretable solution by restricting the dimensionality of the local regression models. We will show that our approach achieves a similar predictive performance on benchmark problems compared to standard regression methods – while the model complexity of our approach is reduced.
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Nusser, S., Otte, C., Hauptmann, W. (2008). An EM-Based Piecewise Linear Regression Algorithm. In: Corchado, E., Abraham, A., Pedrycz, W. (eds) Hybrid Artificial Intelligence Systems. HAIS 2008. Lecture Notes in Computer Science(), vol 5271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87656-4_58
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DOI: https://doi.org/10.1007/978-3-540-87656-4_58
Publisher Name: Springer, Berlin, Heidelberg
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