Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 468405, 11 pages
http://dx.doi.org/10.1155/2014/468405
Research Article

A Procedure for Extending Input Selection Algorithms to Low Quality Data in Modelling Problems with Application to the Automatic Grading of Uploaded Assignments

1Computer Science Department, Universidad de Oviedo, Sedes Departamentales, Edificio 1, Campus de Viesques, 33203 Gijón, Spain
2Computer Science Department, Universidad de Granada, C/Periodista Daniel Saucedo Arana s/n, 18071 Granada, Spain
3Statistics Department, E. U. I. T. Industrial, Universidad de Oviedo, Módulo 1, Planta 4, Campus de Viesques, 33203 Gijón, Spain

Received 11 March 2014; Revised 26 May 2014; Accepted 9 June 2014; Published 7 July 2014

Academic Editor: Anand Paul

Copyright © 2014 José Otero et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. Arnow and O. Barshay, “On-line programming examinations using Web to teach,” in Proceedings of the 4th Annual SIGCSE/SIGCUE ITiCSE Conference on Innovation and Technology in Computer Science Education (ITiCSE '99), pp. 21–24, 1999. View at Publisher · View at Google Scholar
  2. K. A. Reek, “A software infrastructure to suppor t introductory computer science courses,” in Proceedings of the 27th SIGCSE Technical Symposium on Computer Science Education (SIGCSE '96), K. J. Klee, Ed., pp. 125–129, ACM, New York, NY, USA, 1996.
  3. A. Kurnia, A. Lim, and B. Cheang, “Online judge,” Computers & Education, vol. 36, no. 4, pp. 299–315, 2001. View at Google Scholar
  4. B. Cheang, A. Kurnia, A. Lim, and W. Oon, “On automated grading of programming assignments in an academic institution,” Computers and Education, vol. 41, no. 2, pp. 121–131, 2003. View at Publisher · View at Google Scholar · View at Scopus
  5. T. Wang, X. Su, P. Ma, Y. Wang, and K. Wang, “Ability-training-oriented automated assessment in introductory programming course,” Computers and Education, vol. 56, no. 1, pp. 220–226, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Vujosevic-Janicic, M. Nikolić, D. Tošić, and V. Kuncak, “Software verification and graph similarity for automated evaluation of students assignments,” Information and Software Technology, vol. 55, no. 6, pp. 1004–1016, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. F. Jurado, M. Redondo, and M. Ortega, “Using fuzzy logic applied to software metrics and test cases to assess programming assignments and give advice,” Journal of Network and Computer Applications, vol. 35, no. 2, pp. 695–712, 2012. View at Google Scholar
  8. J. Casillas, F. Martínez-López, and F. Martínez, “Fuzzy association rules for estimating consumer behaviour models and their application to explaining trust in internet shopping,” Fuzzy Economic Review, vol. 9, no. 2, pp. 3–26, 2004. View at Google Scholar
  9. J. Casillas and F. J. Martínez-López, “Mining uncertain data with multiobjective genetic fuzzy systems to be applied in consumer behaviour modelling,” Expert Systems with Applications, vol. 36, no. 2, pp. 1645–1659, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. L. Sanchez, I. Couso, and J. Casillas, “Genetic learning of fuzzy rules based on low quality data,” Fuzzy Sets and Systems, vol. 160, no. 17, pp. 2524–2552, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  11. H. Prade and D. Dubois, “Fuzzy sets—a convenient fiction for modeling vagueness and possibility,” IEEE Transactions on Fuzzy Systems, vol. 2, no. 1, pp. 16–21, 1994. View at Publisher · View at Google Scholar · View at Scopus
  12. I. Couso and L. Sanchez, “Higher order models for fuzzy random variables,” Fuzzy Sets and Systems, vol. 159, no. 3, pp. 237–258, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  13. I. Couso, S. Montes, and P. Gil, “The necessity of the strong α-cuts of a fuzzy set,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 9, no. 2, pp. 249–262, 2001. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  14. R. Battiti, “Using mutual information for selecting features in supervised neural net learning,” IEEE Transactions on Neural Networks, vol. 5, no. 4, pp. 537–550, 1994. View at Publisher · View at Google Scholar · View at Scopus
  15. Y. Saeys, T. Abeel, and Y. Peer, “Robust feature selection using ensemble feature selection techniques,” in Machine Learning and Knowledge Discovery in Databases, W. Daelemans, B. Goethals, and K. Morik, Eds., vol. 5212 of Lecture Notes in Computer Science, pp. 313–325, Springer, Berlin, Germany, 2008. View at Google Scholar
  16. L. Sánchez and J. Otero, “Learning fuzzy linguistic models from low quality data by genetic algorithms,” in Proceedings of the IEEE International Conference on Fuzzy Systems, pp. 1–6, July 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. H. Ishibuchi, O. Kuwajima, and Y. Nojima, “Relation between pareto-optimal fuzzy rules and pareto-optimal fuzzy rule sets,” in Proceedings of the 1st IEEE Symposium of Computational Intelligence in Multicriteria Decision Making (MCDM '07), pp. 42–49, IEEE, Honolulu, Hawaii, April 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Abdellatief, A. B. M. Sultan, A. A. A. Ghani, and M. A. Jabar, “A mapping study to investigate component-based software system metrics,” Journal of Systems and Software, vol. 86, no. 3, pp. 587–603, 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. B. Kitchenham, “What's up with software metrics?—a preliminary mapping study,” Journal of Systems and Software, vol. 83, no. 1, pp. 37–51, 2010. View at Publisher · View at Google Scholar · View at Scopus
  20. I. Samoladas, I. Stamelos, L. Angelis, and A. Oikonomou, “Open source software development should strive for even greater code maintainability,” Communications of the ACM, vol. 47, no. 10, pp. 83–87, 2004. View at Publisher · View at Google Scholar · View at Scopus
  21. M. Halstead, Elements of Software Science, Elsevier, North-Holland, 1975.
  22. A. Abran, Software Metrics and Software Metrology, Wiley-IEEE Computer Society Press, 2010.
  23. T. J. McCabe, “A complexity measure,” IEEE Transactions on Software Engineering, vol. SE-2, no. 4, pp. 308–320, 1976. View at Google Scholar · View at MathSciNet · View at Scopus
  24. A. H. Watson, McCabe Complexity, Software Development Systems Management Development, Auerbach, 1995.
  25. N. Pizzi, A. Demko, and W. Pedrycz, “The analysis of software complexity using stochastic metric selection,” Journal of Pattern Recognition Research, vol. 6, no. 1, pp. 19–31, 2011. View at Publisher · View at Google Scholar
  26. T. M. Khoshgoftaar, K. Gao, and A. Napolitano, “A comparative study of different strategies for predicting software quality,” in Proceedings of the 23rd International Conference on Software Engineering and Knowledge Engineering (SEKE '11), pp. 65–70, July 2011. View at Scopus
  27. P. Sallis, A. Aakjaer, and S. MacDonell, “Software forensics: old methods for a new science,” in Proceedings of the International Conference on Software Engineering: Education and Practice (SEEP ' 96), pp. 481–484, IEEE Computer Society, Washington, DC, USA, 1996.
  28. G. Bortolan and R. Degani, “A review of some methods for ranking fuzzy subsets,” Fuzzy Sets and Systems, vol. 15, no. 1, pp. 1–19, 1985. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  29. B. Boehm, B. Clark, E. Horowitz et al., “Cost models for future softwa re life cycle processes: COCOMO 2.0.,” Annals of Software Engineering, vol. 1, no. 1, pp. 57–94, 1995. View at Publisher · View at Google Scholar
  30. L. Breiman, L. J. Friedman, A. Olshen, and C. Stone, Classification and Regression Trees, Wadsworth, 1984.
  31. S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, New York, NY, USA, 2nd edition, 1998.
  32. A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Statistics and Computing, vol. 14, no. 3, pp. 199–222, 2004. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  33. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus