Classification of Streaming Fuzzy DEA Using Self-Organizing Map
الموضوعات :Alireza Alinezhad 1 , Mohammad Amin Adibi 2 , Amine Tohidi 3
1 - Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 - Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
3 - Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
الکلمات المفتاحية: Classification, Data Envelopment Analysis, Mathematical Programming, Streaming Fuzzy Data, Self-Organizing Map,
ملخص المقالة :
The classification of fuzzy data is considered as the most challenging areas of data analysis and the complexity of the procedures has been obstacle to the development of new methods for fuzzy data analysis. However, there are significant advances in modeling systems in which fuzzy data are available in the field of mathematical programming. In order to exploit the results of the researches on fuzzy mathematical programming, in this study, a new fuzzy data classification method based on data envelopment analysis (DEA) is provided when fuzzy data are imported as a stream. The proposed method can classify data that changes are created in their behavioral pattern over time using updating the criteria of predicting fuzzy data class. To reduce computational time, fuzzy self-organizing map (SOM) is used to compress incoming data. The new method was tested by simulated data and the results indicated the feasibility of this technique in the face of uncertain and variable conditions.
Adibi, M. A., & Shahrabi, J. (2015). Online Anomaly Detection Based on Support Vector Clustering. International Journal of Computational Intelligence Systems, 8(4), 735-746.
Adibi, M. A., & Shahrabi, J. A time-varying quadratic programming for online clustering of streaming data. Pattern Analysis and Applications, 1-10. doi:10.1007/s10044-017-0608-9
Aliahmadipour, L., Torra, V., & Eslami, E. (2017). On Hesitant Fuzzy Clustering and Clustering of Hesitant Fuzzy Data. In Fuzzy Sets, Rough Sets, Multisets and Clustering (pp. 157-168). Springer International Publishing.
Chen, T. Y., Ku, T. C., &Tsui, C. W. (2008). Determining attribute importance based on triangular and trapezoidal fuzzy numbers in (z) fuzzy measures. In The 19th international conference on multiple criteria decision making (pp. 75-76).
Colubi, A., González-Rodríguez, G., Gil, M. Á., &Trutschnig, W. (2011). Nonparametric criteria for supervised classification of fuzzy data.International Journal of Approximate Reasoning, 52(9), 1272-1282.
Forghani, Y., SadoghiYazdi, H., &Effati, S. (2013). Classification of fuzzy data based on the support vector machines. Expert Systems, 30(5), 403-417.
Guerrero-Enamorado, A., Morell, C., Noaman, A. Y., & Ventura, S. (2016). An algorithm evaluation for discovering classification rules with gene expression programming. International Journal of Computational Intelligence Systems, 9(2), 263-280.
Jiang, C., & Lin, W. (2015). DEARank: a data-envelopment-analysis-based ranking method. Machine Learning, 101(1-3), 415-435.
León, T., Liern, V., Ruiz, J. L., &Sirvent, I. (2003). A fuzzy mathematical programming approach to the assessment of efficiency with DEA models.Fuzzy sets and systems, 139(2), 407-419.
Lofberg, J. (2010). Yalmip.
Mena-Torres, D., & Aguilar-Ruiz, J. S. (2014). A similarity-based approach for data stream classification.Expert Systems with Applications, 41(9), 4224-4234.
Pendharkar, P. C. (2011). A hybrid radial basis function and data envelopment analysis neural network for classification.Computers & Operations Research, 38(1), 256-266.
Pendharkar, P. C., &Troutt, M. D. (2014). Interactive classification using data envelopment analysis.Annals of Operations Research, 214(1), 125-141.
Quost, B., &Denœux, T. (2016). Clustering and classification of fuzzy data using the fuzzy EM algorithm.Fuzzy Sets and Systems, 286, 134-156.
Shahraki, H., &Zahiri, S. H. (2015, March). Particle swarm classifier for fuzzy data sets. In Artificial Intelligence and Signal Processing (AISP), 2015 International Symposium on (pp. 295-299). IEEE.
Wei, Q., Chang, T. S., & Han, S. (2014). Quantile–DEA classifiers with interval data.Annals of Operations Research, 217(1), 535-563.
Yan, H., & Wei, Q. (2011). Data envelopment analysis classification machine.Information Sciences, 181(22), 5029-5041.
Yang, L. H., Wang, Y. M., Lan, Y. X., Chen, L., & Fu, Y. G. (2017). A data envelopment analysis (DEA)-based method for rule reduction in extended belief-rule-based systems. Knowledge-Based Systems, 123, 174-187.
Yazdi, H. S., Yazdi, M. S., &Vahedian, A. (2009). Fuzzy Bayesian classification of LR Fuzzy numbers. International Journal of Engineering and Technology, 1(5), 415.
Zhang, Y., Yang, C., Yang, A., Xiong, C., Zhou, X., & Zhang, Z. (2015). Feature selection for classification with class-separability strategy and data envelopment analysis. Neurocomputing, 166, 172-184.
