Abstract
Multiple criteria decision making (MCDM) is an approach to rank the alternatives with respect to the different attributes. Several MCDM approaches were used to select the best alternatives of meta-heuristic modeling under the soft-computing domain where the true best alternative is not known. Alternatives are artificial neural network models, selection of which is difficult based on many conflicting performance measures. This paper addresses two new methods for MCDM, using the concept of Minkowski distance and based on technique for order preference by similarity to ideal solution philosophy. The performances of these two methods are compared with four other methods considering real-life data and simulated experiments.
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Sinha BK, Shah KR (2003) On some aspects of data integration techniques with environmental applications. Environmetrics 14:409–416. doi:10.1002/env.595
Dawson CW, Wilby RL (2001) Hydrological modeling using artificial neural networks. Prog Phys Geogr 25(1):80–108
Hwang CL, Yoon K (1981) Multiple attribute decision making: methods and applications. Springer, New York
Zeleny M (1982) Multiple criteria decision making. McGraw-Hill, New York
Bishop C (1995) Neural networks for pattern recognition. University Press, Oxford
Carpenter W, Barthelemy J (1993) A comparison of polynomial approximations and artificial neural nets as response surfaces. Struct Optim 5(3):166–174. doi:10.1007/BF01743353
Dumortier C, Lehert P (1999) Statistical modelling of mechanical tensile properties of steels by using neural networks and multivariate data analysis. ISIJ Int 39(10):980–985. doi:10.2355/isijinternational.39.980
Dutta M, Mukhopadhyay A, Chakrabarti S (2004) Effect of galvanising parameters on spangle size investigated by data mining technique. ISIJ Int 44(1):129–138. doi:10.2355/isijinternational.44.129
Chen YA, Chou C-C, Lu X, Slate EH, Peck K, Xu W, Voit EO, Almeida JS (2006) A multivariate prediction model for microarray cross-hybridization. BMC Bioinformatics 7(101):1471–2105. doi:10.1186/1471-2105-7-101
Matsuoka K, Kawamoto M (1994) a neural network that self-organizes to perform three operations related to principal component analysis. Neural Netw 7(5):753–765. doi:10.1016/0893-6080(94)90097-3
Oja E (1982) A simplified neuron model as a principal components analyser. J Math Biol 15:267–273. doi:10.1007/BF00275687
Sanger TD (1989) Optimal unsupervised learning in a single-layer linear feedforward neural network. Neural Netw 2:459–473. doi:10.1016/0893-6080(89)90044-0
Kung SY, Diamantras KI, Taur JS (1994) adaptive principal components extraction APEX and application. IEEE Trans Signal Process 42(5):1245–1259. doi:10.1109/78.295198
Földiak P (1989) Adaptive network for optimal linear feature extraction. In: Proceedings of the international joint conference on neural networks, Washington, vol 1, pp 401–405
Carreira-Perpinan MA (1997) A review of dimension reduction techniques. Technical report CS-96-09, Department of Computer Science, University of Sheffield
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Das, P. In search of best alternatives: a TOPSIS driven MCDM procedure for neural network modeling. Neural Comput & Applic 19, 91–102 (2010). https://doi.org/10.1007/s00521-009-0260-4
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DOI: https://doi.org/10.1007/s00521-009-0260-4