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|Title:||Reliable confidence intervals for software effort estimation||Authors:||Andreou, Andreas S.
|Category:||Electrical Engineering - Electronic Engineering - Information Engineering||Field:||Engineering and Technology||Issue Date:||1-Dec-2009||Source:||CEUR Workshop Proceedings||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.||URI:||http://ktisis.cut.ac.cy/handle/10488/13755||ISSN:||16130073
|Appears in Collections:||Δημοσιεύσεις σε συνέδρια/Conference papers|
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