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A column generation based heuristic algorithm for piecewise linear regression
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DOI：10.1016/j.eswa.2020.114539.png)
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Abstract
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Piecewise linear regression is a powerful and flexible regression technique where the dataset is divided into disjoint partitions and a separate regression is computed for each partition. Here, we consider the piecewise linear regression problem where the data partitioning is performed via a fixed number of break points on a predetermined dimension. We develop a column generation heuristic based on a set partitioning formulation of the problem and evaluate its prediction performance using a mixed integer programming formulation introduced earlier as a benchmark. Our results show that the proposed heuristic displays an efficient and robust performance, and also scales up smoothly as the dataset grows.
Keywords:
Data analytics
Data fitting
Piecewise linear regression
Non-linear regression
Column generation
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