Software Diagnosis Using Fuzzified Attribute Base on Modified MEPA

  • Jr-Shian Chen
  • Ching-Hsue Cheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)


Currently, there are many data preprocess methods, such as data discretization, data cleaning, data integration and transformation, data reduction ... etc. Concept hierarchies are a form of data discretization that can use for data preprocessing. Using discrete data are usually more compact, shorter and more quickly than using continuous ones. So that we proposed a data discretization method, which is the modified minimize entropy principle approach to fuzzify attribute and then build the classification tree. For verification, two NASA software projects KC2 and JM1 are applied to illustrate our proposed method. We establish a prototype system to discrete data from these projects. The error rate and number of rules show that the proposed approaches are both better than other methods.


Membership Function Concept Hierarchy Defect Prediction Model Classification Decision Tree Improve Software Quality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)Google Scholar
  2. 2.
    Liu, H., Hussain, F., Tan, C., Dash, M.: Discretization: An enabling technique. Data Mining and Knowledge Discovery 6(4), 393–423 (2002)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Fenton, N., Pfleeger, S.: Software Metrics - A Rigorous and Practical Approach. Chapmann & Hall, London (1997)Google Scholar
  4. 4.
    Raman, V., Hellerstein, J., wheel, P.: An interactive data cleaning system. In: VLDB, Roma, Italy, pp. 381–390 (2001)Google Scholar
  5. 5.
    Fayyad, U., Shapiro, G.P., Smyth, P.: The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM 39, 27–34 (1996)CrossRefGoogle Scholar
  6. 6.
    Mitra, S., Pal, S.K., Mitra, p.: Data mining in soft computing framework: A survey. IEEE Trans. Neural Networks 13(1), 3–14 (2002)CrossRefGoogle Scholar
  7. 7.
    Cai, Y., Cercone, N., Han, J.: Knowledge discovery in databases: an attribute-oriented approach. In: VLDB, pp. 547–559 (1992)Google Scholar
  8. 8.
    Dunham, M.H.: Data Mining: Introductory and Advanced Topics. Prentice Hall, Upper Saddle River (2003)Google Scholar
  9. 9.
    Ross Quinlan, J.: Induction of decision trees. Machine Learning 1, 81–106 (1986)Google Scholar
  10. 10.
    Ross Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)Google Scholar
  11. 11.
    Yager, R., Filev, D.: Template-based fuzzy system modeling. Intelligent and Fuzzy Sys. 2, 39–54 (1994)Google Scholar
  12. 12.
    Ross, T.J.: Fuzzy logic with engineering applications, International edition. McGraw-Hill, USA (2000)Google Scholar
  13. 13.
    Christensen, R.: Entropy minimax sourcebook. Entropy Ltd, Lincoln (1980)Google Scholar
  14. 14.
    Sayyad Shirabad, J., Menzies, T.J.: The PROMISE Repository of Software Engineering Databases. School of Information Technology and Engineering, University of Ottawa, Canada (2005), available:
  15. 15.
    Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)Google Scholar
  16. 16.
    Khoshgoftaar, T.M., Seliya, N., Gao, K.: Detecting noisy instances with the rule-based classification model. Intelligent Data Analysis 9(4), 347–364 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jr-Shian Chen
    • 1
    • 2
  • Ching-Hsue Cheng
    • 1
  1. 1.Department of Information ManagementNational Yunlin University of Science and Technology, 123, Section 3Touliu, YunlinTaiwan
  2. 2.Department of Computer Science and Information ManagementHUNGKUANG UniversityShalu, TaichungTaiwan

Personalised recommendations