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Journal of Engineering and Applied Sciences
Year: 2019 | Volume: 14 | Issue: 7 | Page No.: 2276-2291
DOI: 10.36478/jeasci.2019.2276.2291  
Experimental Comparison of Complexities of Artificial Neural Network and Kolmogorov Distance for News Article Classification
Temitayo M. Fagbola , Ibrahim A. Adeyanju , Ayodele Oloyede , Sijuade Adeyemi , Olatayo M. Olaniyan and Bolaji A. Omodunbi
 
Abstract: In today’s growing complex multi-domain and multi-language program specifications, the incessant failure of most software products is a great challenge. This is due to the high complexities associated with these products beyond reliable performance tolerable limits which in turn makes their maintenance impossible in an effective manner. On one hand, the inherent complexities of software systems are often ignored at design and implementation stages and on the other hand for such complex software products, maintenance cost becomes very huge in the face of high defect rate. Consequently, evaluating and managing software complexities is key to ensuring that software products are highly understandable, testable, reliable, maintainable, scalable, efficient and cost-effective. In this study, the complexity associated with the use of Artificial Neural Network (ANN) and Kolmogorov Complexity Distance Measure (KCDM) for solving news article classification problem was measured using Lines of Code (LoC) and halstead measure. Similarly, the accuracy and computational efficiency of these classifiers were also determined using true positive rate and testing time measures, respectively. British Broadcasting Corporation (BBC) News dataset composed of 2225 documents corresponding to entertainment, sport, education/technology, politics and business was used for experimental purpose. Based on the results obtained, ANN produced higher classification accuracy at higher complexity and classification time while KCDM yielded lower complexity and testing time but suffers from low classification accuracy. Hence, from a developer’s point of view, complexity measurement at design and implementation stages of the software development life cycle is pertinent in other to identify complex designs and codes for refactoring to prevent high software defect rate. From the stakeholders end, effective decisions can be reached by considering the tradeoffs between complexity and accuracy of software products to be used in real-life settings.
 
How to cite this article:
Temitayo M. Fagbola, Ibrahim A. Adeyanju, Ayodele Oloyede, Sijuade Adeyemi, Olatayo M. Olaniyan and Bolaji A. Omodunbi, 2019. Experimental Comparison of Complexities of Artificial Neural Network and Kolmogorov Distance for News Article Classification. Journal of Engineering and Applied Sciences, 14: 2276-2291.
DOI: 10.36478/jeasci.2019.2276.2291
URL: http://medwelljournals.com/abstract/?doi=jeasci.2019.2276.2291