International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 5, No. 10, 8 Sep. 2013
Cover page and Table of Contents: PDF (size: 638KB)
Performance Analysis of Classification Methods and Alternative Linear Programming Integrated with Fuzzy Delphi Feature Selection
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Author(s)
Bahram Izadi
1,*
Bahram Ranjbarian
1
Saeedeh Ketabi
1
Faria Nassiri-Mofakham
2
Index Terms
Fuzzy Delphi Feature Selection, Customer Classification Problem, Multi-Group Linear Programming, Artificial Neural Network, Logistic Regression, Support Vector Machine
Abstract
Among various statistical and data mining discriminant analysis proposed so far for group classification, linear programming discriminant analysis have recently attracted the researchers’ interest. This study evaluates multi-group discriminant linear programming (MDLP) for classification problems against well-known methods such as neural networks, support vector machine, and so on. MDLP is less complex compared to other methods and does not suffer from local optima. However, sometimes classification becomes infeasible due to insufficient data in databases such as in the case of an Internet Service Provider (ISP) small and medium-sized market considered in this research. This study proposes a fuzzy Delphi method to select and gather required data. The results show that the performance of MDLP is better than other methods with respect to correct classification, at least for small and medium-sized datasets.
Cite This Paper
Bahram Izadi, Bahram Ranjbarian, Saeedeh Ketabi, Faria Nassiri-Mofakham, "Performance Analysis of Classification Methods and Alternative Linear Programming Integrated with Fuzzy Delphi Feature Selection", International Journal of Information Technology and Computer Science(IJITCS), vol.5, no.10, pp.9-20, 2013. DOI:10.5815/ijitcs.2013.10.02
Reference
[1]Pai, D. R., Lawrence, K. D., Klimberg, R. K., and Lawrence, S. M., (2012) "Experimental comparison of parametric, non-parametric, and hybrid multigroup classification." Expert Systems with Applications. vol. 39: p. 8593-8603.
[2]Youssef, S. and Rebai, A., (2007) "Comparison between statistical approaches and linear programming for resolving classification problem." International Mathematical Forum. vol. 63: p. 3125 - 3141.
[3]Michie, D. and Spiegelhalter, D., (1994) "Machine Learning, Neural and Statistical Classification." 1994: Taylor.
[4]Dyche, J. and Dych, J., (2001) "The CRM handbook: a business guide to customer relationship management." 2001: Reading, MA: Addison-Wesley.
[5]Johnson, R. and Wichern, D., (1988) "Applied Multivariate Statistical Approach." 1988, Englwood Cliffs, NJ: Prentice-Hall.
[6]Meyers, L., Gamst, G., and Guarino, A., (2006) "Applied Multivariate Research: Design and Interpretation." 2006, Thousand Oaks, CA.: Sage Publications, Inc.
[7]Mangasarian, O., (1965) " Linear and nonlinear separation of patterns by linear programming." Journal of Operations Research. vol. 13: p. 444-452.
[8]McCarty, J. and Hastak, M., (2007) "Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression." Journal of Business Research. vol. 60: p. 656–662.
[9]Shmueli, G., Patel, N., and Bruce., P., (2006) "Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner." 2006, NJ: John Wiley and Sons, Inc.
[10] Blattberg, R. C., Kim, B., and Neslin, S. A., (2008) "Database marketing: analyzing and managing customers." 2008, New York: Springer.
[11]Morrison, D., (1969) "On the Interpretation of Discriminant Analysis." Journal of Marketing Research. vol. 6: p. 156-163.
[12]Celebi, D. and Bayraktar, D., (2008) "An integrated neural network and data envelopment analysis for supplier evaluation under incomplete information." Expert Systems with Applications. vol. 35: p. 1698–1710.
[13]Vapnik, V., (1995) "The Nature of Statistical Learning Theory." 1995, NY: Springer.
[14]Flach, P., (2001) "On the State of the Art in Machine Learning: A Personal Review." Artificial Intelligence. vol. 131no.(1-2): p. 199–222.
[15]witten, I. and Frank, E., (2005) "Data Mining, Practical Machine Learning Tools and Techniques." 2005, Oxford, UK: Elsevier.
[16]Freed, N. and Glover, F., (1981) "Simple but powerful goal programming models for discriminant problems." European Journal of Operational Research vol. 7: p. 44-66.
[17]Sun, M., (2010) "linear Programming approaches for multiple-Class discriminant and Classification Analysis." International Journal of Strategic Decision Sciences. vol. 1no.(1): p. 57-80.
[18]Lam, K., Choo, E., and Moy, J., (1996) "Improved Linear Programming Formulations for the Multi-Group Discriminant Problem.." Journal of the Operational Research Society. vol. 47no.(12): p. 1526-1529.
[19]Kotsiantis, S. and Pintelas, P., (2004) "Recent Advances in Clustering: A Brief Survey." WSEAS Transactions on Information Science and Applications. vol. 1: p. 73--81.
[20]MacQueen, J. "Some methods for classification and analysis of multivariate observations." 1967. Berkeley: University of California Press.
[21]Kiang, M. Y., Hu, M. Y., and Fisher, D. M., (2006) "An extended self-organizing map network for market segmentation—a telecommunication example." Decision Support Systems vol. 42: p. 36-47.
[22]Birant, D., "Data Mining Using RFM Analysis," in Knowledge Oriented Applications in Data Mining, Funatsu, K., Hasegawa, K., Editor. 2011, InTech: Rijeka, Croatia. p. 91-108. www.spss.com
[23]Hsu, Y. L., Lee, C. H., and Kreng, V. B., (2010) "The application of fuzzy Delphi Method and fuzzy AHP in lubricant regenerative technology selection." Expert Systems with Applications. vol. 37: p. 419-425.
[24]Harold A., M., T., (1975) "The Delphi Method: Techniques and Applications." 1975, Reading: Addison-Wesley.
[25]Noorderhaben, N., (1995) "Strategic decision making." 1995, UK: Addison-Wesley.
[26]Dunteman, G., (1984) "Introduction to multivariate analysis." 1984, Thousand Oaks, CA: Sage Publications.