Paper Info
Title
Radial basis function network using intuitionistic fuzzy C means for software cost estimation
Abstract
Software development has become an important activity for many modern organisations. Software engineers have become more and more concerned about accurately predicting the cost and quality of software product under development. In the last few decades many software cost estimation models have been developed but no model has proved to be successful at effectively and consistently predicting software development cost. In this paper we propose the use of Radial Basis Function Network RBFN for software cost estimation using Intuitionistic Fuzzy C Means IFCM with Gaussian potential functions. This technique selects the most desirable cluster centres, thereby increasing the clustering accuracy which results in improved software cost estimations. A comparison of RBFN using IFCM, Fuzzy C Means FCM and conventional COCOMO model is presented. The datasets used in our study are the COCOMO81 dataset and NASA93 dataset. Experimental results are given to show the effectiveness of the proposed method.
Year
DOI
Venue
2013
10.1504/IJCAT.2013.054305
IJCAT
Keywords
Field
DocType
software product,radial basis function network,software development,software cost estimation model,intuitionistic fuzzy c,cocomo81 dataset,software engineer,software cost estimation,intuitionistic fuzzy c means,fuzzy c means fcm,software development cost,improved software cost estimation,fuzzy clustering
Fuzzy clustering,Data mining,Cost estimation models,Radial basis function network,Computer science,Fuzzy logic,Cost estimate,Software,Artificial intelligence,COCOMO,Software development,Machine learning
Journal
Volume
Issue
ISSN
47
1
0952-8091
Citations 
PageRank 
References 
6
0.61
23
Authors
3
Name
Order
Citations
PageRank
Anupama Kaushik1152.56
A. K. Soni2202.25
Rachna Soni3111.74