A column generation based heuristic algorithm for piecewise linear regression

Tunc H., GENÇ B.

EXPERT SYSTEMS WITH APPLICATIONS, vol.171, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 171
  • Publication Date: 2021
  • Doi Number: 10.1016/j.eswa.2020.114539
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, Public Affairs Index, Civil Engineering Abstracts
  • Hacettepe University Affiliated: Yes


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.