After Zadeh introduced the concept of z-number scientists in various fields have shown keen interest applying this applications. In applications z-numbers, to compare two a ranking procedure is essential. While few functions been already proposed literature there need evolve some more good functions. paper, novel function for z-numbers proposed- "the Momentum Ranking Function"(MRF). Also, game ...

This paper investigates the theoretical relation between loss criteria and the optimal ranking functions driven by the criteria in bipartite ranking. In particular, the relation between AUC maximization and minimization of ranking risk under a convex loss is examined. We characterize general conditions for ranking-calibrated loss functions in a pairwise approach, and show that the best ranking ...

Many online or local data sources provide powerful querying mechanisms but limited ranking capabilities. For instance, PubMed allows users to submit highly expressive Boolean keyword queries, but ranks the query results by date only. However, a user would typically prefer a ranking by relevance, measured by an Information Retrieval (IR) ranking function. The naive approach would be to submit a ...

Traditionally, skyline and ranking queries have been treated separately as alternative ways of discovering interesting data in potentially large datasets. While ranking queries adopt a specific scoring function to rank tuples, skyline queries return the set of non-dominated tuples and are independent of attribute scales and scoring functions. Ranking queries are thus less general, but usually c...

Relevance feedback image retrieval is an effective scheme bridging the gap between low-level features and high-level concepts. It is essentially a multiquery ranking problem where the user submitted image and provided positive examples are considered as queries. Most of the existing approaches either merge the multiple queries into a single query or consider them independently, and then the geo...

We consider the problem of maximizing an unknown function f over a compact and convex set X ⊂ R using as few observations f(x) as possible. We observe that the optimization of the function f essentially relies on learning the induced bipartite ranking rule of f . Based on this idea, we relate global optimization to bipartite ranking which allows to address problems with high dimensional input s...

The discovery of invariants and ranking functions plays a central role in program verification. In our previous work, we investigated invariant generation and non-linear ranking function discovering of polynomial programs by reduction to semi-algebraic systems solving. In this paper we will first summarize our results on the two topics and then show how to generalize the approach to discovering...