In this study we focus on improvement performance of a cue based Motor Imagery Brain Computer Interface (BCI). For this purpose, data fusion approach is used on results of different classifiers to make the best decision. At first step Distinction Sensitive Learning Vector Quantization method is used as a feature selection method to determine most informative frequencies in recorded signals and ...

In practical term any result obtained using an ordered weighted averaging (OWA) operator heavily depends upon the method to determine the weighting vector. Several approaches for obtaining the associated weights have been suggested in the literature, in which none of them took into account the preference of alternatives. This paper presents a method for determining the OWA weights when the pref...

We investigate manipulation of the Borda voting rule, as well as two elimination style voting rules, Nanson’s and Baldwin’s voting rules, which are based on Borda voting. We argue that these rules have a number of desirable computational properties. For unweighted Borda voting, we prove that it is NP-hard for a coalition of two manipulators to compute a manipulation. This resolves a long-standi...

Commonly used GIS combination operators such as Boolean conjunction/disjunction and weighted linear combination can be generalized to the ordered weighted averaging (OWA) family of operators. This multicriteria evaluation method allows decision-makers to define a decision strategy on a continuum between pessimistic and optimistic strategies. Recently, OWA has been introduced to GIS-based decisi...

Relevance Learning Vector Quantization (RLVQ) (introduced in [1]) is a variation of Learning Vector Quantization (LVQ) which allows a heuristic determination of relevance factors for the input dimensions. The method is based on Hebbian learning and defines weighting factors of the input dimensions which are automatically adapted to the specific problem. These relevance factors increase the over...

GIS-based Multicriteria Decision Analysis (GIS-MCDA) provides a rich collection of techniques and procedures for landslide susceptibility mapping. In this study landslide susceptibility was evaluated by applying different analytical GIS techniques based on Ordered Weighted Averaging (OWA) criteria. The OWA-MCDA is complemented by a Monte Carlo Simulation (MCS) and a Dempster-Shafer Theory (DST)...

The ordered weighted averaging (OWA) operator by Yager has received more and more attention since its appearance. One key point in the OWA operator is to determine its associated weights. Among numerous methods that have appeared in the literature, we notice the maximum entropy OWA (MEOWA) weights that are determined by taking into account two appealing measures characterizing the OWA weights. ...

We provide a natural extension of the Borda count to the n-dimensional spatial context, an algorithm to find the spatial Borda winner based on the notion of an inverse Borda count, the result that the Borda winner and the Condorcet winner coincide in unidimensional space when all alternatives on a line are feasible, results that show that in multi-dimensional space the Borda winner and the Cond...

In a report published in 2007 UNICEF measured six dimensions of child well-being for the majority of the economically advanced nations. No overall scores are given, but countries are listed in the order of their average rank on the dimensions, which are therefore implicitly assigned 'equal importance'. In this study we take 'equal importance' to mean that the final aggregation is symmetrical in...

* On leave of absence from the Department of Electronics and Computers, Transylvania University of Braşov. Abstract – Ordered Weighted Aggregation (OWA) operators represent a distinct family of aggregation operators and were introduced by Yager in [1]. They compute a weighted sum of a number of criteria that must be satisfied. The central element of the OWA operators is that the criteria are re...