Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/13755
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Andreou, Andreas S. | - |
dc.contributor.author | Papadopoulos, Harris | - |
dc.contributor.author | Papatheocharous, Efi | - |
dc.date.accessioned | 2019-05-23T11:23:31Z | - |
dc.date.available | 2019-05-23T11:23:31Z | - |
dc.date.issued | 2009-04 | - |
dc.identifier.citation | 5th IFIP Conference on Artificial Intelligence Applications and Innovations, AIAI 2009; Thessaloniki; Greece; 23 April 2009 through 25 April 2009; Code 100720 | en_US |
dc.identifier.issn | 1613-0073 | - |
dc.description | CEUR Workshop Proceedings Volume 475, 2009, Pages 211-220 | en_US |
dc.description.abstract | This paper deals with the problem of software effort estimation through the use of a new machine learning technique for producing reliable confidence measures in predictions. More specifically, we propose the use of Conformal Predictors (CPs), a novel type of prediction algorithms, as a means for providing effort estimations for software projects in the form of predictive intervals according to a specified confidence level. Our approach is based on the well-known Ridge Regression technique, but instead of the simple effort estimates produced by the original method, it produces predictive intervals that satisfy a given confidence level. The results obtained using the proposed algorithm on the COCOMO, Desharnais and ISBSG datasets suggest a quite successful performance obtaining reliable predictive intervals which are narrow enough to be useful in practice. | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.subject | Forecasting | en_US |
dc.subject | Learning systems | en_US |
dc.subject | Conformal predictor | en_US |
dc.title | Reliable confidence intervals for software effort estimation | en_US |
dc.type | Conference Papers | en_US |
dc.collaboration | University of Cyprus | en_US |
dc.collaboration | Frederick University | en_US |
dc.subject.category | Electrical Engineering - Electronic Engineering - Information Engineering | en_US |
dc.country | Cyprus | en_US |
dc.subject.field | Engineering and Technology | en_US |
dc.publication | Peer Reviewed | en_US |
dc.relation.conference | International Conference on Artificial Intelligence Applications and Innovations | en_US |
dc.identifier.scopus | 2-s2.0-84887253684 | en |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/84887253684 | en |
dc.contributor.orcid | #NODATA# | en |
dc.contributor.orcid | #NODATA# | en |
dc.contributor.orcid | #NODATA# | en |
dc.relation.volume | 475 | en |
cut.common.academicyear | 2008-2009 | en_US |
item.fulltext | No Fulltext | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_c94f | - |
item.openairetype | conferenceObject | - |
item.languageiso639-1 | en | - |
crisitem.author.dept | Department of Electrical Engineering, Computer Engineering and Informatics | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.orcid | 0000-0001-7104-2097 | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
Appears in Collections: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
CORE Recommender
SCOPUSTM
Citations
50
22
checked on Nov 6, 2023
Page view(s)
257
Last Week
5
5
Last month
11
11
checked on May 13, 2024
Google ScholarTM
Check
Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.