A comprehensive workflow for enhancing business bankruptcy prediction

• Type: Chapter or Part of a Book
• Date: 2014

Authors

Sarmento, R (Author) Other; Trigo, L (Author) Other; Fonseca, L (Author) Other

Book

Title: Integration of Data Mining in Business Intelligence Systems

Pages: 216-238

ISBN: 978-146666477-7

Electronic ISBN: 978-146666478-4

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific