Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Karel Dejaeger"'
Publikováno v:
IEEE Transactions on Software Engineering. 39:237-257
Software testing is a crucial activity during software development and fault prediction models assist practitioners herein by providing an upfront identification of faulty software code by drawing upon the machine learning literature. While especiall
Publikováno v:
Information & Management. 50:43-58
Recent studies have indicated that companies are increasingly experiencing Data Quality (DQ) related problems as more complex data are being collected. To address such problems, the literature suggests the implementation of a Total Data Quality Manag
Publikováno v:
IEEE transactions on software engineering
A predictive model is required to be accurate and comprehensible in order to inspire confidence in a business setting. Both aspects have been assessed in a software effort estimation setting by previous studies. However, no univocal conclusion to whi
Publikováno v:
Decision Support Systems. 51:141-154
An important objective of data mining is the development of predictive models. Based on a number of observations, a model is constructed that allows the analysts to provide classifications or predictions for new observations. Currently, most research
Publikováno v:
Journal of systems and software
Software fault and effort prediction are important tasks to minimize costs of a software project. In software effort prediction the aim is to forecast the effort needed to complete a software project, whereas software fault prediction tries to identi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::58b4b06c5799358fa0662b600b71ba34
https://lirias.kuleuven.be/handle/123456789/471625
https://lirias.kuleuven.be/handle/123456789/471625
Publikováno v:
European Journal of Operational Research
European Journal of Operational Research, Elsevier, 2012, 218 (2), pp.548-562. ⟨10.1016/j.ejor.2011.11.022⟩
European Journal of Operational Research, 2012, 218 (2), pp.548-562. ⟨10.1016/j.ejor.2011.11.022⟩
European Journal of Operational Research, Elsevier, 2012, 218, pp.548-562
European Journal of Operational Research, Elsevier, 2012, 218 (2), pp.548-562. ⟨10.1016/j.ejor.2011.11.022⟩
European Journal of Operational Research, 2012, 218 (2), pp.548-562. ⟨10.1016/j.ejor.2011.11.022⟩
European Journal of Operational Research, Elsevier, 2012, 218, pp.548-562
As a consequence of the heightened competition on the education market, the management of educational institutions often attempts to collect information on what drives student satisfaction by e.g. organizing large scale surveys amongst the student po
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ea09778fb08a9ca46d893a5fb8445bff
https://halshs.archives-ouvertes.fr/halshs-01929190
https://halshs.archives-ouvertes.fr/halshs-01929190
Publikováno v:
European journal of operational research
Customer churn prediction models aim to indicate the customers with the highest propensity to attrite, allowing to improve the efficiency of retention campaigns to prevent customers from churning, and to reduce the costs associated with churn. Althou
Recent studies indicated that companies are increasingly experiencing data quality (DQ) related problems resulting from their increased data collection efforts. Addressing these concerns requires a clear definition of DQ but typically, DQ is only bro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::11689db1bc6a5aa4d76cdaad5b686a44
https://lirias.kuleuven.be/handle/123456789/351216
https://lirias.kuleuven.be/handle/123456789/351216
Autor:
Mikkel Riis, Tuomas Eerola, Lieve Goedhuys, Karel Dejaeger, Seppe vanden Broucke, Rainer Wehkamp, Bart Baesens
Publikováno v:
SSRN Electronic Journal.
Machine learning (ML) techniques are becoming commonplace in business and research alike. With the automatization of data collection e fforts, evermore data is being captured, rendering the task of extracting insightful patterns increasingly challeng
In software defect prediction, predictive models are estimated based on various code attributes to assess the likelihood of software modules containing errors. Many classification methods have been suggested to accomplish this task. However, associat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::939f291ac0958c5ffd41d0b367f2cedb
https://lirias.kuleuven.be/handle/123456789/296322
https://lirias.kuleuven.be/handle/123456789/296322