Multi-objective evolutionary feature selection for online sales forecasting

F. Jiménez, G. Sánchez, J. M. García, G. Sciavicco, L. Miralles

Research output: Contribution to journalArticlepeer-review

98 Citations (Scopus)

Abstract

Sales forecasting uses historical sales figures, in association with products characteristics and peculiarities, to predict short-term or long-term future performance in a business, and it can be used to derive sound financial and business plans. By using publicly available data, we build an accurate regression model for online sales forecasting obtained via a novel feature selection methodology composed by the application of the multi-objective evolutionary algorithm ENORA (Evolutionary NOn-dominated Radial slots based Algorithm) as search strategy in a wrapper method driven by the well-known regression model learner Random Forest. Our proposal integrates feature selection for regression, model evaluation, and decision making, in order to choose the most satisfactory model according to an a posteriori process in a multi-objective context. We test and compare the performances of ENORA as multi-objective evolutionary search strategy against a standard multi-objective evolutionary search strategy such as NSGA-II (Non-dominated Sorted Genetic Algorithm), against a classical backward search strategy such as RFE (Recursive Feature Elimination), and against the original data set.

Original languageEnglish
Pages (from-to)75-92
Number of pages18
JournalNeurocomputing
Volume234
DOIs
Publication statusPublished - 19 Apr 2017
Externally publishedYes

Keywords

  • Feature selection
  • Multi-objective evolutionary algorithms
  • Online sales forecasting
  • Random forest
  • Regression model

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