NDLI: Software effort prediction using unsupervised learning (clustering) and functional link artificial neural networks

Content Provider	IEEE Xplore Digital Library
Author	Benala, T.R. Dehuri, S. Mall, R. ChinnaBabu, K.
Copyright Year	2012
Description	Author affiliation: Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur (Dehuri, S.) \|\| Department Of Computer Science & Engineering, Anil Neerukonda Institute of Technology & Sciences, Visakhapatnam-531162, India (ChinnaBabu, K.) \|\| Department of Systems Engineering, Ajou University, Woncheon-dong, Yeongtong-gu, Suwon 443-749, Korea (Mall, R.) \|\| Department Of Computer Science & Engineering, Anil Neerukonda Institute Of Technology & Sciences, Visakhapatnam-531162, India (Benala, T.R.)
Abstract	Software cost estimation continues to be an area of concern for managing of software development industry. We use unsupervised learning (e.g., clustering algorithms) combined with functional link artificial neural networks for software effort prediction. The unsupervised learning (clustering) indigenously divide the input space into the required number of partitions thus eliminating the need of ad-hoc selection of number of clusters. Functional link artificial neural networks (FLANNs), on the other hand is a powerful computational model. Chebyshev polynomial has been used in the FLANN as a choice for functional expansion to exhaustively study the performance. Three real life datasets related to software cost estimation have been considered for empirical evaluation of this proposed method. The experimental results show that our method could significantly improve prediction accuracy of conventional FLANN and has the potential to become an effective method for software cost estimation.
Starting Page	115
Ending Page	120
File Size	476470
Page Count	6
File Format	PDF
ISBN	9781467348065
e-ISBN	9781467348058
DOI	10.1109/WICT.2012.6409060
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-30
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Algorithm design and analysis Density-Based Spatial Clustering of Application with Noise (DBSCAN) Clustering algorithms Estimation Approximation algorithms Software Software Cost Estimation (SCE) Unsupervised K-Window Clustering (UKW) Unsupervised learning Functional Link Artificial Neural Network (FLANN)
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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