Assessment of a Multi-Strategy Classifier for an Embedded Software System

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http://doi.ieeecomputersociety.org/10.1109/ICTAI.2006.35

18th IEEE International Conference on ...

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	Taghi M. Khoshgoftaar, Kehan Gao, "Assessment of a Multi-Strategy Classifier for an Embedded Software System," Tools with Artificial Intelligence, IEEE International Conference on, pp. 651-658, 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06), 2006.

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	@article{ 10.1109/ICTAI.2006.35, author = {Taghi M. Khoshgoftaar and Kehan Gao}, title = {Assessment of a Multi-Strategy Classifier for an Embedded Software System}, journal ={Tools with Artificial Intelligence, IEEE International Conference on}, volume = {0}, year = {2006}, issn = {1082-3409}, pages = {651-658}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICTAI.2006.35}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

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	TY - CONF JO - Tools with Artificial Intelligence, IEEE International Conference on TI - Assessment of a Multi-Strategy Classifier for an Embedded Software System SN - 1082-3409 SP651 EP658 A1 - Taghi M. Khoshgoftaar, A1 - Kehan Gao, PY - 2006 KW - rule-based model KW - case-based learning KW - genetic algorithm KW - multi-strategy classifier KW - software quality classification VL - 0 JA - Tools with Artificial Intelligence, IEEE International Conference on ER -

Taghi M. Khoshgoftaar, Florida Atlantic University, USA

Kehan Gao, Eastern Connecticut State University, USA

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,Windowsc-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.

Index Terms:

rule-based model, case-based learning, genetic algorithm, multi-strategy classifier, software quality classification

Citation:

Taghi M. Khoshgoftaar, Kehan Gao, "Assessment of a Multi-Strategy Classifier for an Embedded Software System," ictai, pp.651-658, 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06), 2006

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