Knowledge discovery in the prediction of bankruptcy

R.J. Almeida, S.M. Vieira, D.V. Milea, U. Kaymak, J.M. Costa Sousa, da

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)
3 Downloads (Pure)

Abstract

Knowledge discovery in databases (KDD) is the process of discovering interesting knowledge from large amounts of data. However, real-world datasets have problems such as incompleteness, redundancy, inconsistency, noise, etc. All these problems affect the performance of data mining algorithms. Thus, preprocessing techniques are essential in allowing knowledge to be extracted from data. This work presents a real world application of knowledge discovery in databases, with the objective of prediction of bankruptcy. For this task fuzzy classification models based on fuzzy clustering are used, which are developed solely from numerical data. This data set has missing values, extreme values and also presents a much smaller bankruptcy class than the not bankruptcy class, which makes it a challenging problem in the scope of KDD.
Original languageEnglish
Title of host publicationProceedings of the 2009 International Fuzzy Systems Association World Congress and 2009 European Society of Fuzzy Logic and Technology Conference, Lisbon, Portugal, July 20-24, 2009
EditorsJ.P. Carvalho, D. Dubois, U. Kaymak
Place of PublicationLisbon
PublisherEusflat
Pages1785-1790
ISBN (Print)978-989-95079-6-8
Publication statusPublished - 2009
EventJoint 13th International Fuzzy Systems Association World Congress and 6th European Society of Fuzzy Logic and Technology Conference (IFSA-EUSFLAT 2009), July 20-24, 2009, Lisbon, Portugal - Lisbon, Portugal
Duration: 20 Jul 200924 Jul 2009

Conference

ConferenceJoint 13th International Fuzzy Systems Association World Congress and 6th European Society of Fuzzy Logic and Technology Conference (IFSA-EUSFLAT 2009), July 20-24, 2009, Lisbon, Portugal
Abbreviated titleIFSA-EUSFLAT 2009
CountryPortugal
CityLisbon
Period20/07/0924/07/09

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