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Abstract:
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The concept of pareto optimality has been utilized in the fields such as engineering, economics, and machine learning to understand fluid dynamics, consumer behavior, and identifying parameters that best optimize a set of m criteria (multiobjective optimization). During the process of model selection statisticians are often concerned with the model which has the single most optimal criterion (eg. AIC, R^2) before continuing to check a number of other diagnostics. This strategy is multi-objective in nature but single-objective in its numeric execution. This poster will first introduce the general framework of Pareto optimality and common strategies to attain and visualize its solutions for regression subset selection. A feasible solutions algorithm will be introduced as well as how the algorithm can be applied to multio-bjective problems in subset selection. A short example will be provided as well.
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