Market segmentation is a multicriterion problem. This dissertation addresses the multicriterion nature of market segmentation with a new unified segmentation model that is derived from a multiobjective conceptual framework. The unified model elegantly solves the intrinsic antagonistic problem of market segmentation by generating a set of Pareto optimal solutions that represent different tradeoffs among multiple conflicting objectives. This dissertation develops an innovative implementation named Multicriterion Market Segmentation using Evolutionary Algorithm (MMSEA). Based on multiobjective...

In this research, parametric software cost estimation models and their related calibration methods have been analyzed, especially for the COCOMO model and the Bayesian calibration approach. This research combines machine learning techniques and statistical techniques. With this approach, the prediction powers of the COCOMO parametric software cost model are shown to be significantly improved while the variability is decreased with respect to the dataset being analyzed. This research studies not only the accuracy but also the variances of the model and the variables. It can improve the...