Ranking Functions

Using Directional Distance Functions to Determine Ranking Ranges in Cross-efficiency Evaluations

A Fully Fuzzy Method of Network Data Envelopment Analysis for Assessing Revenue Efficiency Based on Ranking Functions

The purpose of this paper is to evaluate the revenue efficiency in the fuzzy network data envelopment analysis‎. ‎Precision measurements in real-world data are not practically possible‎, ‎so assuming that data is crisp in solving problems is not a valid assumption‎. ‎One way to deal with imprecise data is fuzzy data‎. ‎In this paper‎, ‎linear ranking functions are used to transform the full fuz...

Extend a ranking method of trapezoidal fuzzy numbers to all fuzzy numbers by a weighting functions

Recently Abbasbandy and Hajjari (Computers and Mathematics with Applications57 (2009) 413-419) have introduced a ranking method for the trapezoidalfuzzy numbers. This paper extends theirs method to all fuzzy numbers,which uses from a defuzzication of fuzzy numbers and a general weightingfunction. Extended method is interesting for ranking all fuzzy numbers, and itcan be applied for solving and ...

A Prior Work on Listwise Ranking

The exchangeability assumption as defined in this paper on ranking functions seems intuitively natural, and indeed, specific ranking functions previously proposed in the literature are all exchangeable. While pointwise ranking functions are vacuously exchangeable, we now discuss two specifically listwise ranking functions previously proposed by [22] and [26] in light of our representation theor...

Essential Loss: Bridge the Gap between Ranking Measures and Loss Functions in Learning to Rank

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...

Policy Iteration-Based Conditional Termination and Ranking Functions

Termination analyzers generally synthesize ranking functions or relations, which represent checkable proofs of their results. In [23], we proposed an approach for conditional termination analysis based on abstract fixpoint computation by policy iteration. This method is not based on ranking functions and does not directly provide a ranking relation, which makes the comparison with existing appr...