Improved xDeepFM With Single Value Decomposition and Attention Mechanism

Autor: Yiwan Zhang, Zhan Wang, Inwhee Joe

Publikováno v: IEEE Access, Vol 12, Pp 106447-106454 (2024)

Due to the sheer volume and variety of data in industrial manufacturing, manually creating features can be costly. Therefore, appropriate feature processing methods become crucial. Most existing feature processing methods abstract feature engineering

Externí odkaz: https://doaj.org/article/580026dfa49e44358edfb4ef55664399

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Clustering and Classification Based on Distributed Automatic Feature Engineering for Customer Segmentation

Autor: Zne-Jung Lee, Chou-Yuan Lee, Li-Yun Chang, Natsuki Sano

Publikováno v: Symmetry, Vol 13, Iss 9, p 1557 (2021)

To beat competition and obtain valuable information, decision-makers must conduct in-depth machine learning or data mining for data analytics. Traditionally, clustering and classification are two common methods used in machine mining. For clustering,

Externí odkaz: https://doaj.org/article/08f2380a866346abb7a1b07ffd69d1db

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

Autor: Nascimento, Alexandre Moreira, de Melo, Vinicius Veloso, Muller Queiroz, Anna Carolina, Brashear-Alejandro, Thomas, Meirelles, Fernando de Souza

Publikováno v: Revista de Contabilidade e Organizações; Vol. 14 (2020); e171481
Revista de Contabilidade e Organizações; v. 14 (2020); e171481
Revista de contabilidade e organizações
Universidade de São Paulo (USP)
instacron:USP

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 techniq

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______3056::f80487806b6e742a92daa042784f3d96
https://www.revistas.usp.br/rco/article/view/171481