A comprehensive workflow for enhancing business bankruptcy prediction

• Tipo: Capítulo ou Parte de Livro
• Data: 2014

Autores

Sarmento, R (Autor) Outra; Trigo, L (Autor) Outra; Fonseca, L (Autor) Outra

Livro

Título: Integration of Data Mining in Business Intelligence Systems

Páginas: 216-238

ISBN: 978-146666477-7

ISBN Eletrónico: 978-146666478-4

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00G-TY2

DOI: 10.4018/978-1-4666-6477-7.ch011

Abstract (EN): Forecasting enterprise bankruptcy is a critical area for Business Intelligence. It is a major concern for investors and credit institutions on risk analysis. It may also enable the sustainability assessment of critical suppliers and clients, as well as competitors and the business environment. Data Mining may deliver a faster and more precise insight about this issue. Widespread software tools offer a broad spectrum of Artificial Intelligence algorithms and the most difficult task may be the decision of selecting that algorithm. Trying to find an answer for this decision in the relatively large amount of available literature in this area with so many options, advantages, and pitfalls may be as informative as distracting. In this chapter, the authors present an empirical study with a comprehensive Knowledge Discovery and Data Mining (KDD) workflow. The proposed classifier selection automation selects an algorithm that has better prediction performance than the most widely documented in the literature. © 2015, IGI Global.

Idioma: Inglês

Tipo (Avaliação Docente): Científica