The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
A Comprehensive Workflow for Enhancing Business Bankruptcy Prediction
Abstract
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.
Related Content
|
Mohammed Adi Al Battashi, Mohamad A. M. Adnan, Asyraf Isyraqi Bin Jamil, Majid Adi Al-Battashi.
© 2026.
30 pages.
|
|
Potchong M. Jackaria, Al-adzran G. Sali, Hana An L. Alvarado, Rashidin H. Moh. Jiripa, Al-sabrie Y. Sahijuan.
© 2026.
26 pages.
|
|
Elizabeth Gross.
© 2026.
30 pages.
|
|
Siti Nazleen Abdul Rabu, Xie Fengli, Ng Man Yi.
© 2026.
44 pages.
|
|
Mohammed Abdul Wajeed.
© 2026.
30 pages.
|
|
Aldammien A. Sukarno, Al-adzkhan N. Abdulbarie, Wati Sheena M. Bulkia, Potchong M. Jackaria.
© 2026.
24 pages.
|
|
Abdulla Sultan Binhareb Almheiri, Humaid Albastaki, Hanadi Alrashdan.
© 2026.
26 pages.
|
|
|