论文信息 - Non-monotone averaging aggregation

Non-monotone averaging aggregation

We advance the theory of aggregation operators and introduce non-monotone aggregation methods based on minimization of a penalty for inputs disagreements. The application in mind is processing data sets which may contain noisy values. Our aim is to filter out noise while at the same time preserve signs of unusual values. We review various methods of robust estimators of location, and then introduce a new estimator based on penalty minimisation.

Daniel Paternain | Shui Yu | Gleb Beliakov

[1] Gleb Beliakov,et al. Aggregation functions based on penalties , 2010, Fuzzy Sets Syst..

[2] Mariano Eriz. Aggregation Functions: A Guide for Practitioners , 2010 .

[3] R. Mesiar,et al. ”Aggregation Functions”, Cambridge University Press , 2008, 2008 6th International Symposium on Intelligent Systems and Informatics.

[4] S. A. Piyavskii. An algorithm for finding the absolute extremum of a function , 1972 .

[5] R. Yager,et al. UNDERSTANDING THE MEDIAN AS A FUSION OPERATOR , 1997 .

[6] Radko Mesiar,et al. Quantitative weights and aggregation , 2004, IEEE Transactions on Fuzzy Systems.

[7] G. Beliakov. Extended cutting angle method of global optimization , 2008 .

[8] Gleb Beliakov,et al. Implementation of novel methods of global and nonsmooth optimization: GANSO programming library , 2007 .

[9] Gleb Beliakov. Cutting angle method – a tool for constrained global optimization , 2004, Optim. Methods Softw..

[10] Peter J. Rousseeuw,et al. Robust Regression and Outlier Detection , 2005, Wiley Series in Probability and Statistics.

[11] P. Rousseeuw. Least Median of Squares Regression , 1984 .

[12] Vicenç Torra,et al. Modeling decisions - information fusion and aggregation operators , 2007 .