Open AccessArtificial intelligence applied to small businesses: the use of automatic feature engineering and machine learning for more accurate planning
Author(s) -
Alexandre Nascimento,
Vinícius Veloso de Melo,
Anna Carolina Muller Queiroz,
Thomas Brashear Alejandro,
Fernando de Souza Meirelles
Publication year - 2020
Publication title -
revista de contabilidade e organizações
Language(s) - English
Resource type - Journals
ISSN - 1982-6486
DOI - 10.11606/issn.1982-6486.rco.2020.171481
Subject(s) - feature engineering , computer science , artificial intelligence , machine learning , feature (linguistics) , classifier (uml) , revenue , resampling , support vector machine , business intelligence , data mining , deep learning , philosophy , linguistics , accounting , business
The purpose of this study is to develop a predictive model that increases the accuracy of business operational planning using data from a small business. By using Machine Learning (ML) techniques feature expansion, resampling, and combination techniques, it was possible to address several existing limitations in the available research. Then, the use of the novel technique of feature engineering allowed us to increase the accuracy of the model by finding 10 new features derived from the original ones and constructed automatically through the nonlinear relationships found between them. Finally, we built a rule-based classifier to predict the store's revenue with high accuracy. The results show the proposed approach open new possibilities for ML research applied to small and medium businesses.