TY  - JOUR
T1  - Unsupervised Learning Technique Using Hybrid Optimization for Non-Functional Requirements Classification
AU - Mahalakshmi, K. AU - Manikandan, S. AU - Nithyanantham, S. AU - Sathiyaseelan, K.A. AU - Sudhakar, P. 
JO  - Asian Journal of Information Technology
VL  - 15
IS  - 9
SP  - 1457
EP  - 1467
PY  - 2016
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2016.1457.1467
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2016.1457.1467
KW  - Support vector machine
KW  -non-functional requirements
KW  -radial basis function
KW  -artificial bee colony
KW  -differential evolution
KW  -hybrid optimization
AB  - Problems in software engineering area can be solved mathematically. In this study, Support Vector Machine (SVM) are utilized to categorize Non-Functional Requirements (NFRs). The NFR-Classifiers are used to identify cross-cutting predominant framework toward disintegration found in necessities particular or early plan records are proposed. Optimization is used to acquire the best results under given circumstances. In order to improve the efficiency of SVM, Artificial Bee Colony (ABC) technique with Differential Evolution (DE) is used. The proposed technique improves the classification accuracy by 90.54% than existing techniques.
ER  -