Bipartite ranking aims to learn a real-valued ranking function that orders positive instances before negative instances. Recent efforts of bipartite ranking are focused on optimizing ranking accuracy at the top of the ranked list. Most existing approaches are either to optimize task specific metrics or to extend the ranking loss by emphasizing more on the error associated with the top ranked in...

Assume a teacher provides examples one by one. An approximate incremental SVM computes a sequence of classi ers that are close to the true SVM solutions computed on the successive incremental training sets. We show that simple algorithms can satisfy an averaged accuracy criterion with a computational cost that scales as well as the best SVM algorithms with the number of examples. Finally, we ex...

In this paper, we propose a skyline-based recommendation and ranking function. We suppose that some recommender systems, such as hotel recommender systems, are based not only on user preferences but also cost performance. For these kinds of applications, We first extract items with good cost performance and then identify items that users prefer, which reduce the computational cost of the online...

The purpose of this paper is to study fully fuzzy multi-choice multiobjective linear programming problem (FFMMOLPP), in which, right hand side of each constraint has two choices. With the help of ranking function, deviation distance and deviation degree between two fuzzy numbers are defined. A new method is proposed to find the  fuzzy pareto optimal solution of FFMMOLPP which is defuzzified by...

Selecting the best items in a dataset is a common task in data exploration. However, the concept of “best” lies in the eyes of the beholder: different users may consider different attributes more important, and hence arrive at different rankings. Nevertheless, one can remove “dominated” items and create a “representative” subset of the data set, comprising the “best items” in it. A Pareto-optim...

Method: Building a document ranking system involves two key decisions: choosing a retrieval model, and choosing a suitable index representation. The former determines the effectiveness of the system, the latter the efficiency; and each of them affects the other. The impact-based document ranking mechanism described by Anh and Moffat [2] was chosen for our system because of its balance between e...

The existence of alternate optima for the DEA weights may reduce the usefulness of the cross-efficiency evaluation, since the ranking provided depends on the choice of weights that the different DMUs make. In this paper, we develop a procedure to carry out the cross-efficiency evaluation without the need to make any specific choice of DEA weights. The proposed procedure takes into consideration...

In this paper, we have developed a heuristic that uses Data Envelopment Analysis (DEA) to rank Decision Making Units (DMUs) with fuzzy input and output values in the order of their probable efficiency. The heuristic is designed to be used with the raw samples of the fuzzy data, and does not require knowledge of the fuzzy data’s probability distribution. It is meant to be a simple method of diff...

data envelopment analysis (dea) technique now widely use for efficiency evaluation of a set of decision making units (dmus). as regards of the necessity for ranking efficient units different dea models presented each of which has advantages and rank efficient units from special aspects. note that all the existing ranking models have disadvantages, as well and there is not a model in which all t...