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Learning stable weights for data of varying dimension

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Abstract
In this paper we develop a data-driven weight learning method for weighted quasiarithmetic means where the observed data may vary in dimension.
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[1]J. Aczel. On mean values. Bulletin of the American Math.Society, 54, 1948. [2] A. Amo, J. Montero, and E. Molina. Representation of consistent recursive rules. European Journal of Operational Research, 130:29–53, 2001. [3] G. Beliakov. Construction of aggregation functions from data using linear programming. Fuzzy Sets and Systems, 160:65–75, 2009. [4] G. Beliakov and S. James. Stability of weighted penalty-based aggregation functions. Fuzzy Sets and Systems, 226:1–18, 2013. [5] G. Beliakov, A. Pradera, and T. Calvo. Aggregation Functions: A Guide for Practitioners. Springer, Heidelberg, 2007. [6] P. Bloomfield and W. Steiger. Least Absolute Deviations. Theory, Applications and Algorithms. Birkhauser, Boston, Basel, Stuttgart, 1983. [7] H. Bustince, B. de Baets, J. Fern´andez, R. Mesiar,and J. Montero. A generalization of the migrativity property of aggregation functions. Information Sciences, 191:76–85,2012. [8] T. Calvo, G. Mayor, J. Torrens, J. Suñer, M. Mas,and M. Carbonell. Generation of weighting triangles associated with aggregation functions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 8(4):417–451, 2000. [9] V. Cutello and J. Montero. Recursive connective rules. Int. J. Intelligent Systems, 14:3–20, 1999. [10] J. Dujmovic. Aggregation operators and observable properties of human reasoning. [11] J. Dujmovic, G. D. Tre, N. Singh, D. Tomasevich,and R. Yokoohji. Soft computing models in online real estate. In M. Jamshidi, V. Kreinovich,and J. Kacprzyk, editors, Advance Trends in Soft Computing, WCSC 2013, Studies in Fuzziness and Soft Computing 312, pages 77–91. Springer,2013. [12] J. J. Dujmovic. The problem of missing data in LSP aggregation. In S. G. et al., editor, Advances in Computational Intelligence, IPMU 2012, Part III, CCIS 299, pages 336 – 346. Springer, Catania,Italy, 2012. [13] D. Gomez and J. Montero. A discussion on aggregation operators. Kybernetika, 40:107–120, 2004. [14] D. Gomez, K. Rojas, J. Montero, J. Rodrıguez,and G. Beliakov. Consistency and stability in aggregation operators, an application to missing data problems. Int. J. Computational Intelligence Systems, 7:595–604, 2014. [15] M. Grabisch, J.-L. Marichal, R. Mesiar, and E. Pap. Aggregation Functions. Cambridge University Press,Cambridge, 2009. [16] D. Nettleton and V. Torra. A comparison of active set method and genetic algorithm approaches for learning weighting vectors in some aggregation operators.International Journal of Intelligent Systems, 16(9):1069–1083, 2001. [17] R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria,2011. http://www.R-project.org/. [18] K. Rojas, D. Gomez, J. Montero, and J. T.Rodrıguez. Strictly stable families of aggregation operators. Fuzzy Sets and Systems, 228:44 – 63,2013. [19] K. Rojas, D. Gomez, J. Rodriguez, and J. Montero. Some properties of consistency in the families of aggregation functions. Advances in Intelligent and Soft Computing,107:169–176, 2011. [20] V. Torra and Y. Narukawa. The h-index and the number of citations: Two fuzzy integrals. IEEE Transactions on Fuzzy Systems, 16(3):795–797,2008. [21] Y. Torra and V. Narukawa. Modeling Decisions.Information Fusion and Aggregation Operators.Springer, 2007. [22] R. R. Yager and A. Rybalov. Noncommutative self-identity aggregation. Fuzzy Sets and Systems,85:73–82, 1997.