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Hybrid morphological methodology for software development cost estimation
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DOI：10.1016/j.eswa.2011.11.077.png)
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Abstract
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In this paper we propose a hybrid methodology to design morphological-rank-linear (MRL) perceptrons in the problem of software development cost estimation (SDCE). In this methodology, we use a modified genetic algorithm (MGA) to optimize the parameters of the MRL perceptron, as well as to select an optimal input feature subset of the used databases, aiming at a higher accuracy level for SDCE problems. Besides, for each individual of MGA, a gradient steepest descent method is used to further improve the MRL perceptron parameters supplied by MGA. Finally, we conduct an experimental analysis with the proposed methodology using six well-known benchmark databases of software projects, where two relevant performance metrics and a fitness function are used to assess the performance of the proposed methodology, which is compared to classical machine learning models presented in the literature. (C) 2011 Elsevier Ltd. All rights reserved.
Keywords:
Software development cost estimation
Morphological-rank-linear perceptrons
Genetic algorithms
Hybrid methodologies
Feature selection
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