in this paper, presenting two simple methods for ranking of efficient dmus in dea models that included to add one virtual dmu as ideal dmu and is using the additive model. note that, we use an ideal point just for comparing efficient dmus with. although these methods are simple, they have ability for ranking all efficient dmus, extreme points and the others, also they are capable of ranking the...

Various methods, known as Multiple Criteria Decision Making Methods (MCDM), have been proposed to assist decision makers in the process of ranking alternatives. Given the variability of available methods, choosing an MCDM ranking method is a difficult task. There are key factors in the process of choosing an MCDM method such as: (i) available time; (ii) effort required by a given approach; (iii...

Ranking fuzzy numbers plays a very important role in the decision process, data analysis, and applications. The last few decades have seen a large number of methods investigated for ranking fuzzy numbers. The most commonly used approach for ranking fuzzy numbers is ranking indices based on the centroids of fuzzy numbers. However, there are some weaknesses associated with these indices. This pap...

Many real-world machine learning applications require a ranking of cases, in addition to their classi cation. While classi cation rules are not a good representation for ranking, the human comprehensibility aspect of rules makes them an attractive option for many ranking problems where such model transparency is desired. There have been numerous studies on ranking with decision trees, but not m...

Ranking fuzzy numbers plays a very important role in decision making and some other fuzzy application systems. Many different methods have been proposed to deal with ranking fuzzy numbers. Constructing ranking indexes based on the centroid of fuzzy numbers is an important case. But some weaknesses are found in these indexes. The purpose of this paper is to give a new ranking index to rank vario...

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...

we consider biobjective shortest path problems in networks with fuzzy arc lengths. considering the available studies for single objective shortest path problems in fuzzy networks, using a distance function for comparison of fuzzy numbers, we propose three approaches for solving the biobjective prob- lems. the rst and second approaches are extensions of the labeling method to solve the sing...

Learning to rank has become an important research topic in machine learning. While most learning-to-rank methods learn the ranking functions by minimizing the loss functions, it is the ranking measures (such as NDCG and MAP) that are used to evaluate the performance of the learned ranking functions. In this work, we reveal the relationship between ranking measures and loss functions in learning...