Affiliations: [a] LIAAD - INESC TEC/FEP, Universidade do Porto, Porto, Portugal | [b] CESE - INESC TEC/FEUP, Universidade do Porto, Porto, Portugal
Correspondence:
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Corresponding author: Fábio Pinto, LIAAD - INESC TEC/FEP, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal. E-mail:[email protected]
Abstract: Data Mining (DM) researchers often focus on the development
and testing of models for a single decision (e.g., direct mailing, churn
detection, etc.). In practice, however, multiple decisions have often to be made
simultaneously which are not independent and the best global solution is often
not the combination of the best individual solutions. This problem can be
addressed by searching for the overall best solution by using optimization
methods based on the predictions made by the DM models. We describe one case
study were this approach was used to optimize the layout of a retail store in
order to maximize predicted sales. A metaheuristic is used to search different
hypothesis of space allocations for multiple product categories, guided by the
predictions made by regression models that estimate the sales for each category
based on the assigned space. We test three metaheuristics and three regression
algorithms on this task. Results show that the Particle Swam Optimization
method guided by the models obtained with Random Forests and Support Vector
Machines models obtain good results. We also provide insights about the relationship between the correctness of the regression models and the metaheuristics performance.
Keywords: Data Mining, metaheuristics, retail, space allocation
