@conference {Idri2015976,
	title = {RBFN network based models for estimating software development effort: A cross-validation study},
	booktitle = {Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015},
	year = {2015},
	note = {cited By 0},
	pages = {976-983},
	abstract = {Software effort estimation is very crucial and there is always a need to improve its accuracy as much as possible. Several estimation techniques have been developed in this regard and it is difficult to determine which model gives more accurate estimation on which dataset. Among all proposed methods, the Radial Basis Function Neural (RBFN) networks models have presented promising results in software effort estimation. The main objective of this research is to evaluate the RBFN networks construction based on both hard and fuzzy C-means clustering algorithms using cross-validation approach. The objective of this replication study is to investigate if the RBFN-based models learned from the training data are able to estimate accurately the efforts of yet unseen data. This evaluation uses two historical datasets, namely COCOMO81 and ISBSG R8. {\textcopyright} 2015 IEEE.},
	doi = {10.1109/SSCI.2015.142},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964949962\&doi=10.1109\%2fSSCI.2015.142\&partnerID=40\&md5=af0dde3af393745967961e241dd05385},
	author = {Idri, A.a and Hassani, A.a and Abran, A.b}
}
@conference {Idri2013483,
	title = {Assessing RBFN-based software cost estimation models},
	booktitle = {Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE},
	volume = {2013-January},
	number = {January},
	year = {2013},
	note = {cited By 2},
	pages = {483-487},
	abstract = {This paper is concerned with the design of the neural networks approach, especially Radial Basis Function Neural (RBFN) networks, for software effort estimation models. The study firstly focuses on the construction of the RBFN middle layer composed of receptive fields, using two clustering techniques: hard C-means and fuzzy C-means. Thereafter, we evaluate and compare the performance of effort estimation models that use an RBFN construction-based either on hard or fuzzy C-means. This study uses the ISBSG dataset and confirms the usefulness of an RBFN-based on fuzzy C-means for software effort estimation. Copyright {\textcopyright} 2013 by Knowledge Systems Institute Graduate School.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937716255\&partnerID=40\&md5=26b8de65f7c08367f5351ab898bf97a8},
	author = {Idri, A.a and Hassani, A.a and Abran, A.b}
}
