Multi-objective evolutionary feature selection for online sales forecasting

Jiménez, Fernando; Sánchez, Gracia; García, José M.; Sciavicco, Guido; Miralles-Pechuán, Luis

doi:10.1016/j.neucom.2016.12.045

Multi-objective evolutionary feature selection for online sales forecasting

Journal

Neurocomputing

ISSN

0925-2312

Date Issued

2017

Author(s)

Jiménez, Fernando

Sánchez, Gracia

García, José M.

Sciavicco, Guido

Miralles-Pechuán, Luis

Type

Resource Types::text::journal::journal article

DOI

10.1016/j.neucom.2016.12.045

URL

https://scripta.up.edu.mx/handle/20.500.12552/4407

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.