Abstract
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.
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References
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)
Liu, H., Hussain, F., Tan, C., Dash, M.: Discretization: An enabling technique. Data Mining and Knowledge Discovery 6(4), 393–423 (2002)
Fenton, N., Pfleeger, S.: Software Metrics - A Rigorous and Practical Approach. Chapmann & Hall, London (1997)
Raman, V., Hellerstein, J., wheel, P.: An interactive data cleaning system. In: VLDB, Roma, Italy, pp. 381–390 (2001)
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)
Mitra, S., Pal, S.K., Mitra, p.: Data mining in soft computing framework: A survey. IEEE Trans. Neural Networks 13(1), 3–14 (2002)
Cai, Y., Cercone, N., Han, J.: Knowledge discovery in databases: an attribute-oriented approach. In: VLDB, pp. 547–559 (1992)
Dunham, M.H.: Data Mining: Introductory and Advanced Topics. Prentice Hall, Upper Saddle River (2003)
Ross Quinlan, J.: Induction of decision trees. Machine Learning 1, 81–106 (1986)
Ross Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
Yager, R., Filev, D.: Template-based fuzzy system modeling. Intelligent and Fuzzy Sys. 2, 39–54 (1994)
Ross, T.J.: Fuzzy logic with engineering applications, International edition. McGraw-Hill, USA (2000)
Christensen, R.: Entropy minimax sourcebook. Entropy Ltd, Lincoln (1980)
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: http://promise.site.uottawa.ca/SERepository
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Khoshgoftaar, T.M., Seliya, N., Gao, K.: Detecting noisy instances with the rule-based classification model. Intelligent Data Analysis 9(4), 347–364 (2005)
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Chen, JS., Cheng, CH. (2006). Software Diagnosis Using Fuzzified Attribute Base on Modified MEPA. In: Ali, M., Dapoigny, R. (eds) Advances in Applied Artificial Intelligence. IEA/AIE 2006. Lecture Notes in Computer Science(), vol 4031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11779568_134
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DOI: https://doi.org/10.1007/11779568_134
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35453-6
Online ISBN: 978-3-540-35454-3
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