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Defined solely by means of order-theoretic operations meet (min) and join (max), weighted lattice polynomial functions are particularly useful for modelling data on an ordinal scale. A special case, the discrete Sugeno integral, defined with respect to a nonadditive measure (a capacity), enables accounting for the interdependencies between input variables. However, until recently the problem of identifying the fuzzy measure values with respect to various objectives and requirements has not received a great deal of attention. By expressing the learning problem as the difference of convex functions, we are able to apply DC (difference of convex) optimization methods. Here we formulate one of the global optimization steps as a local linear programming problem and investigate the improvement under different conditions. Abbreviation: DC: difference of convex; LP: linear programming [ABSTRACT FROM AUTHOR]

Abstract: We establish best-possible bounds on the set of quasi-copulas with given degree of non-exchangeability. These bounds are shown to be best-possible bounds as well for the set of copulas with given degree of non-exchangeability, and, consequently, also on the set of bivariate distribution functions of continuous random variables with given margins and given degree of non-exchangeability. Non-exchangeability of a (quasi-)copula is measured in the sense of Nelsen, i.e. proportional to the maximal absolute difference between this (quasi-)copula and its transpose. [Copyright &y& Elsevier]

We present an approach to computing high-breakdown regression estimators in parallel on graphics processing units (GPU). We show that sorting the residuals is not necessary, and it can be substituted by calculating the median. We present and compare various methods to calculate the median and order statistics on GPUs. We introduce an alternative method based on the optimization of a convex function, and show its numerical superiority when calculating the order statistics of very large arrays on GPUs. [ABSTRACT FROM AUTHOR]

We discuss the implementation of a number of modern methods of global and nonsmooth continuous optimization, based on the ideas of Rubinov, in a programming library GANSO. GANSO implements the derivative-free bundle method, the extended cutting angle method, dynamical system-based optimization and their various combinations and heuristics. We outline the main ideas behind each method, and report on the interfacing with Matlab and Maple packages. [ABSTRACT FROM AUTHOR]

We generalize the notion of an absorbent element of aggregation operators. Our construction involves tuples of values that decide the result of aggregation. Absorbent tuples are useful to model situations in which certain decision makers may decide the outcome irrespective of the opinion of the others. We examine the most important classes of aggregation operators in respect to their absorbent tuples, and also construct new aggregation operators with predefined sets of absorbent tuples. [Copyright &y& Elsevier]

Abstract: We generalize the notion of a neutral element of aggregation operators. Our construction involves tuples of values that are neutral with respect to the result of aggregation. Neutral tuples are useful to model situations in which information from different sources, or preferences of several decision makers, cancel each other. We examine many popular classes of aggregation operators in respect to their neutral sets, and also construct new aggregation operators with predefined neutral sets. [Copyright &y& Elsevier]

The need for monotone approximation of scattered data often arises in many problems of regression, when the monotonicity is semantically important. One such domain is fuzzy set theory, where membership functions and aggregation operators are order preserving. Least squares polynomial splines provide great flexbility when modeling non-linear functions, but may fail to be monotone. Linear restrictions on spline coefficients provide necessary and sufficient conditions for spline monotonicity. The basis for splines is selected in such a way that these restrictions take an especially simple form. The resulting non-negative least squares problem can be solved by a variety of standard proven techniques. Additional interpolation requirements can also be imposed in the same framework. The method is applied to fuzzy systems, where membership functions and aggregation operators are constructed from empirical data. [ABSTRACT FROM AUTHOR]

In this work we present an image reduction algorithm based on averaging aggregation functions. We axiomatically define the concepts of image reduction operator and local reduction operator. We study the construction of the latter by means of averaging functions and we propose an image reduction algorithm (image reduction operator). We analyze the properties of several averaging functions and their effect on the image reduction algorithm. Finally, we present experimental results where we apply our algorithm in two different applications, analyzing the best operators for each concrete application. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we introduce an algorithm to aggregate the preference relations provided by experts in multi-expert decision making problems. Instead of using a single aggregation function for the whole process, we start from a set of aggregation functions and select, by means of consensus done through penalty functions, the most suitable aggregation function in order to aggregate the individual preferences for each of the elements. An advantage of the method that we propose is that it allows us to recover the classical methods, just by using a single aggregation function. We also present a generalization of the concepts of restricted dissimilarity function and distance between sets for the case where we are working with a Cartesian product of lattices and use such concepts to build penalty functions. Finally, we propose an algorithm that allows us to choose the best combination of aggregation functions for a multi-expert decision making problem. [Copyright &y& Elsevier]

The process of sleep stage identification is a labour-intensive task that involves the specialized interpretation of the polysomnographic signals captured from a patient’s overnight sleep session. Automating this task has proven to be challenging for data mining algorithms because of noise, complexity and the extreme size of data. In this paper we apply nonsmooth optimization to extract key features that lead to better accuracy. We develop a specific procedure for identifying K-complexes, a special type of brain wave crucial for distinguishing sleep stages. The procedure contains two steps. We first extract “easily classified” K-complexes, and then apply nonsmooth optimization methods to extract features from the remaining data and refine the results from the first step. Numerical experiments show that this procedure is efficient for detecting K-complexes. It is also found that most classification methods perform significantly better on the extracted features. [ABSTRACT FROM PUBLISHER]