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...

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 ...

This paper suggests  a novel approach for ranking the most applicable fuzzy numbers, i.e.  $LR$-fuzzy numbers. Applying the  $alpha$-optimistic values of a fuzzy number, a preference criterion is proposed for ranking fuzzy numbers using the Credibility index. The main properties of the proposed  preference criterion  are also studied.  Moreover, the proposed method is   applied for ranking fuzz...

The ranking aggregation problem is that to establishing a new aggregate ranking given a set of rankings of a finite set of items. This problem is met in various applications, such as the combination of user preferences, the combination of lists of documents retrieved by search engines and the combination of ranked gene lists. In the literature, the ranking aggregation problem has been solved as...

In this paper, we propose the new Ball Ranking Machines (BRMs) to address the supervised ranking problems. In previous work, supervised ranking methods have been successfully applied in various information retrieval tasks. Among these methodologies, the Ranking Support Vector Machines (Rank SVMs) are well investigated. However, one major fact limiting their applications is that Ranking SVMs nee...

A ranking on a graph is an assignment of positive integers to its vertices such that any path between two vertices of the same rank contains a vertex of strictly larger rank. A ranking is locally minimal if reducing the rank of any single vertex produces a non ranking. A ranking is globally minimal if reducing the ranks of any set of vertices produces a non ranking. A ranking is greedy if, for ...

In this paper, we propose a novel ranking framework – Co-Feedback Ranking (CoFRank), which allows two base rankers to supervise each other during the ranking process by providing their own ranking results as feedback to the other parties so as to boost the ranking performance. The mutual ranking refinement process continues until the two base rankers cannot learn from each other any more. The o...

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...

Ranking fuzzy numbers plays a very important role in linguistic decision making and other fuzzy application systems. In spite of many ranking methods, no one can rank fuzzy numbers with human intuition consistently in all cases. Shortcoming are found in some of the convenient methods for ranking triangular fuzzy numbers such as the coefficient of variation (CV index), distance between fuzzy set...

It is important to help researchers find valuable papers from a large literature collection. To this end, many graphbased ranking algorithms have been proposed. However, most of these algorithms suffer from the problem of ranking bias. Ranking bias hurts the usefulness of a ranking algorithm because it returns a ranking list with an undesirable time distribution. This paper is a focused study o...