Developing a New Classification Method Based on a Hybrid Machine Learning and Multi Criteria Decision Making Approach

Document Type : Research Paper

Authors

1 Assistant Prof., Department of Industrial Management, Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran.

2 Department of Industrial Management, Faculty of Management, Electronic Branch, Islamic Azad University, Tehran, Iran.

3 Assistant Prof., Department of Industrial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran.

4 MSc., Department of Information Technology Management, Faculty of Management, Electronic Branch, Islamic Azad University, Tehran, Iran.

Abstract

Objective: According to the capability of analytical network process (ANP) in analysis of different dependencies and feedback relationships among elements of a decision problem, the current research aims to develop an ANP based method for the benchmark classification problems. Since the essential limitation of ANP is the increase of inconsistency in judgment of decision makers along with increase in problem dimensions, genetic algorithm is used to optimize ANP parameters and improve classification accuracy.
Methods: Considering the objective, this study is a developmental research and in term of data analysis, its a quantitative and mathematical modeling one. In this research, first a multi criteria decision making problem is developed based on ANP and in form of a classification problem and then the unknown parameters of a super matrix were calculated by machine learning methods. Next, the most proper values of these parameters which include thresholds of each class and the applied coefficients in the super matrix are estimated based on samples benchmarks or data.The following processes have been conducted througha genetic algorithm. Finally, in order to validate the proposed method, its performance is compared to some frequently used classification methods in the reviewed literature.
Results: The results indicate the very competitive performance of the proposed method compared to known machine learning methods.
Conclusion: Multi-criteria Decision Making Methods (MCDM) are usually used for ranking purposes, however little attention has been paid to their high capabilities. In this paper ANP in combination with genetic algorithm demonstrated an efficient and suitable method in the field of data classification

Keywords


Aragonés-Beltrán, P., Aznar, J., Ferrís-Oñate, J., & García-Melón, M. (2008). Valuation of urban industrial land: an analytic network process approach. European Journal of Operational Research, 185 (1), 322–339.
Baccour, L. (2018). Amended fused TOPSIS-VIKOR for classification (ATOVIC) applied to some UCI data sets. Expert Systems with Applications, 99, 115-125.
Chung, S.H., Lee, H.I., & Pearn, W.L. (2005). Analytic network process (ANP) approach for product mix planning in semiconductor fabricator. International Journal of Production Economics, 96, 15–36.
Daneshvar, A., Homayounfar, M., & Akhavan, E. (2020). Developing a classification Method for Imbalanced Dataset Using Multi-Objective Evolutionary Algorithms. Industrial Management Studies, 17 (4), 161-183. (in Persian)
Daneshvar, A., Homayounfar, M., & Farahmandnejad, A. (2020). Developing an Intelligent Multi Criteria Clustering Method Based on PROMETHEE. Journal of Industrial Management Perspective, 9 (4), 41-61. (in Persian)
Daneshvar, A., Zandieh, M., & Nazemi, J. (2015). An evolutionary method for credit scoring; Preference Disaggregation approach. Industrial Management Studies, 13 (4), 1-34. 
(in Persian)
Demsar, J. (2006), Statistical comparisons of classifiers over multiple datasets. Journal
of Machine Learning Research,
 7, 1–30.
Doumpos, M., Zopounidis, C. (2002). Multicriteria Decision Aid Classification Methods. Kluwer, Dordrecht.
Kartal, H., Oztekin, A., Gunasekaran, A., & Cebi, F. (2016). An integrated decision analytic framework of machine learning with multi-criteria decision making for multi-attribute inventory classification. Computers & Industrial Engineering, 101, 599-613.
Kou, G., Peng, Y., & Wang, G. (2014). Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Information Sciences, 275, 1-12.
Lee, J.W., Kim, S.H. (2000). Using analytic network process and goal programming for interdependent information system project selection, Computers and Operations Research, 27, 367–382.
Marqués, A.I., Garcia, V., & Sánchez, J.S. (2012). Exploring the behavior of base classifiers in credit scoring ensembles. Expert Systems with Applications, 39 (11), 10244-10250.
Massam, B.H. (1988). Multicriteria Decision Making Techniques in Planning, Pergamon, NY.
Meade, L.M., Presley, A. (2002). R&D project selection using the analytic network process, IEEE Transactions on Engineering Management, 49 (1), 59–66.
Meade, L.M., Sarkis, J. (1999). Analyzing organizational project alternatives for agile manufacturing processes: an analytical network approach. International Journal of Production Research, 37(2), 241–261.
Ngai, E.W.T., Hu, Y., Wong, Y.H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50 (3), 559-569.
Niemira, M.P., Saaty, T.L. (2004). An analytic network process model for financial-crisis forecasting. International Journal of Forecasting, 20 (4), 573-587.
Nikam, S.S. (2015). A Comparative Study of Classification Techniques in Data Mining Algorithms. Computer Science and Technology, 8 (1), 13-19.
Ravi, V., Shankar, R., Tiwari, M.K. (2005). Analyzing alternatives in reverse logistics for end-of-life computers: ANP and balanced scorecard approach. Computers & industrial engineering, 48 (2), 327-356.
Saaty, T.L. (1996). The Analytic Network Process, RWS Publications, Pittsburgh.
Saaty, T.L. (2001). Analytic network process. Encyclopedia of Operations Research and Management Science. Springer, 28-35.
Tuzkaya, U.R., Önüt, S. (2008). A fuzzy analytic network process based approach to transportation-mode selection between Turkey and Germany: a case study, Information Sciences, 178 (15), 3133–3146.
Wang, W., Wang, Z., Klir, G.J. (1998). Genetic algorithms for determining fuzzy measures from data, Journal of Intelligent and Fuzzy Systems, 6, 171–183.
Wolfslehner, B., Vacik, H., & Lexer, M.J. (2005). Application of the analytic network process in multicriteria analysis of sustainable forest management, Forest Ecology and Management, 207, 157–170.
Yüksel, I., Dagdeviren, M. (2007). Using the analytic network process (ANP) in a SWOT analysis – a case study for a textile firm, Information Sciences, 177 (16), 3364–3382.
Zarrin Sadaf, M., Daneshvar, A. (2016). An efficient preference learning method based on ELECTRE TRI model for multi-criteria inventory classification. Industrial Management Journal, 8 (2), 191-216. (in Persian).