Kernel methods and rough sets are two general pursuits in the domain of machine learning and intelligent systems. Kernel methods map data into a higher dimensional feature space, where the resulting structure of the classification task is linearly separable; while rough sets granulate the universe with the use of relations and employ the induced knowledge granules to approximate arbitrary conce...

Decision table reduction in KDD refers to the problem of selecting those input feature values that are most predictive of a given outcome by reducing a decision table like database from both vertical and horizontal directions. Fuzzy rough sets has been proven to be a useful tool of attribute reduction (i.e. reduce decision table from vertical direction). However, relatively less researches on d...

In this paper we propose to apply the concept of L-valued kernels (see Bisdorff & Roubens [3, 4]) to the problem of constructing a global ranking from a pairwise L-valued outranking relation defined on a set of decision alternatives as encountered in the fuzzy preference modelling context (see Roy & Bouyssou [11] for instance). Our approach is based on a repetitive selection of best and worst c...

The generalized fuzzy valued θ-Choquet integrals will be established for the given μ-integrable fuzzy valued functions on a general fuzzy measure space, and the convergence theorems of this kind of fuzzy valued integral are being discussed. Furthermore, the whole of integrals is regarded as a fuzzy valued set function on measurable space, the double-null asymptotic additivity and pseudo-double-...

A fuzzy logic controller with interval-valued inference mechanism is presented for the control of distributed parameter system (DPS). Interval-valued fuzzy set, a special case of type-2 fuzzy set, is innovatively designed to cope with the spatial information of distributed parameter systems. The proposed fuzzy logic controller is composed of six modules: fuzzifier, fuzzy composition, rules, inf...