Artificial intelligence applied to small businesses: the use of automatic feature engineering and machine learning for more accurate planning

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 l...

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Bibliographic Details
Published inRevista de contabilidade e organizações Vol. 14; p. e171481
Main Authors Nascimento, Alexandre Moreira, De Melo, Vinicius Veloso, Muller Queiroz, Anna Carolina, Brashear-Alejandro, Thomas, Meirelles, Fernando de Souza
Format Journal Article
LanguageEnglish
Published Universidade de São Paulo (USP) 14.10.2020
Subjects
Artificial intelligence
Automatic feature engineering
Local business
Machine learning
Small business
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Summary: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.
ISSN:1982-6486
1982-6486
DOI:10.11606/issn.1982-6486.rco.2020.171481