Abstract
The concept of Pareto optimality has been utilized in fields such as engineering and economics to understand fluid dynamics and consumer behavior. In machine learning contexts, Pareto-optimality has been used to identify tuning parameters that best optimize a set of m criteria (multi-objective optimization). During the process of regression model selection, data scientists are often concerned with choosing a model which has the best single criterion (e.g., Akaike information criterion (AIC) or R-squared (R2)) before continuing to check a number of other regression model characteristics (e.g., model size, form, diagnostics, and interpretability). This strategy is multi-objective in nature but single objective in its numeric execution. This paper will first introduce a feasible solution algorithm (FSA) and explain how it can be applied to multi-objective problems for regression subset selection. Then we introduce the general framework of Pareto optimality within the regression setting. We then apply the algorithm in a simulation setting where we seek to estimate the first four Pareto boundaries for regression models using two model fit criteria. Finally, we present an application where we use a US communities and crime dataset.
Original language | English |
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Pages (from-to) | 277-284 |
Number of pages | 8 |
Journal | International Journal of Data Science and Analytics |
Volume | 10 |
Issue number | 3 |
DOIs | |
State | Published - Sep 1 2020 |
Bibliographical note
Funding Information:This work was partially supported by the Kentucky Biomedical Research Infrastructure and Institutional Development Award of Biomedical Research Excellence Grant [P20 RR16481] and Multiple Sclerosis Society [PP-1609-25975]
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
Keywords
- Feasible solution
- Multiple
- Objective
- Optimal
- Pareto
- Regression
- Subset selection
ASJC Scopus subject areas
- Information Systems
- Modeling and Simulation
- Computer Science Applications
- Computational Theory and Mathematics
- Applied Mathematics