Dyad ranking is a specific type of preference learning problem, namely the problem of learning a model that is capable of ranking a set of feature vector pairs, called dyads. In this paper a situation is considered where feature vectors consists of identifiers only. A new method based on learning latent-features is introduced for this purpose. The method is evaluated on synthetic data and is ap...

Abstract. The fuzzy set theory has been applied in many fields such as management, engineering etc. In modern management applications ranking using fuzzy numbers are the most important aspect in decision making process. This paper proposes a new method on the incentre of centroids and uses of Euclidean distance to ranking generalized hexagonal fuzzy numbers. We have used a ranking method for or...

Feature selection is a data processing method which aims to select effective feature subsets from original features. based on evolutionary computation (EC) algorithms can often achieve better classification performance because of their global search ability. However, methods using EC cannot get rid invalid features effectively. A small number still exist till the termination algorithms. In this...

 Recently, Khodabakhshi and Aryavash have introduced a ranking method [Applied Mathematics Letters, 25 (2012) 2066-2070.], which is based on an optimistic-pessimistic approach of data envelopment analysis (DEA). This method ranks all decision making units according to a combination of their minimum and maximum possible efficiency scores which are determined by solving two linear programming mo...

We provide a method for the ranking of the reliability of two networks with the same connectance. Our method is based on the Cheeger constant linking the topological property of a network with its spectrum. We first analyze a set of twisted rings with the same connectance and degree distribution, and obtain the ranking of their reliability using their eigenvalue gaps. The results are generalize...

The primary application of collaborate filtering (CF) is to recommend a small set of items to a user, which entails ranking. Most approaches, however, formulate the CF problem as rating prediction, overlooking the ranking perspective. In this work we present a method for collaborative ranking that leverages the strengths of the two main CF approaches, neighborhoodand model-based. Our novel meth...