Abstract :
In this paper, a new classification model, RB2CBL, is proposed. Its structure and methodology are described. By cascading a rule-based (RB) model with two case-based learning (CBL) models, RB2CBL possesses the merits of both RB model and CBL model and restrains their drawbacks. In the RB2CBL model, the parameter optimization of the CBL models is essential, and the embedded genetic algorithm optimizer is used. In our case study, a dataset collected from initial releases of two large, Windowscopy-based embedded system applications, which were used primarily for customizing the configuration of wireless telecommunications products, is processed to investigate and evaluate the models. The results show that, by suitably choosing accuracy settings of the RB model, RB2CBL model outperforms the RB model alone without overfitting. In practice, the RB2CBL model effectively reduced the misclassification rates and improved prediction accuracy for the embedded software system
Keywords :
embedded systems; genetic algorithms; knowledge based systems; learning (artificial intelligence); pattern classification; RB2CBL; case-based learning models; classification model; embedded software system; genetic algorithm optimizer; multistrategy classifier; rule-based model; wireless telecommunications products; Classification tree analysis; Decision trees; Embedded software; Error analysis; Genetic algorithms; Noise level; Predictive models; Software quality; System testing; Training data; case-based learning; genetic algorithm; multi-strategy classifier; rule-based model; software quality classification;
