An empirical validation of software cost estimation models
Communications of the ACM
A Representation Theory for Morphological Image and Signal Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive rank order based filters
Signal Processing
Morphology neural networks: an introduction with applications
Circuits, Systems, and Signal Processing - Special issue: networks for neural processing
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
Empirical Data Modeling in Software Engineering Using Radial Basis Functions
IEEE Transactions on Software Engineering
Efficient SVM Regression Training with SMO
Machine Learning
A meta-model for software development resource expenditures
ICSE '81 Proceedings of the 5th international conference on Software engineering
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Software Effort Estimation Using Machine Learning Techniques with Robust Confidence Intervals
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Journal of Systems and Software
Proceedings of the 2008 ACM symposium on Applied computing
Software project effort estimation with voting rules
Decision Support Systems
Structuring element adaptation for morphological filters
Journal of Visual Communication and Image Representation
Why software fails [software failure]
IEEE Spectrum
MRL-filters: a general class of nonlinear systems and their optimal design for image processing
IEEE Transactions on Image Processing
Hybrid morphological methodology for software development cost estimation
Expert Systems with Applications: An International Journal
Software effort prediction using fuzzy clustering and functional link artificial neural networks
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Hi-index | 12.05 |
Abstract: This work presents a shift-invariant morphological system to solve the problem of software development cost estimation (SDCE). It consists of a hybrid morphological model, which is a linear combination between a morphological-rank (MR) operator (nonlinear) and a Finite Impulse Response (FIR) operator (linear), referred to as morphological-rank-linear (MRL) filter. A gradient steepest descent method to adjust the MRL filter parameters (learning process), using the Least Mean Squares (LMS) algorithm, and a systematic approach to overcome the problem of non-differentiability of the morphological-rank operator are used to improve the numerical robustness of the training algorithm. Furthermore, an experimental analysis is conducted with the proposed system using the NASA software project database, and in the experiments, two relevant performance metrics and an evaluation function are used to assess its performance. The results obtained are compared to models recently presented in literature, showing superior performance of this kind of morphological systems for the SDCE problem.