In this paper attention has been paid to the study of a new ranking procedure for trapezoidal intuitionistic fuzzy number (TRIFN). There are numerous methods for ranking of simple fuzzy numbers but, we lack of effective methods for ranking of intuitionistic fuzzy numbers (IFN). To serve the purpose, the value and ambiguity index of TRIFNs have been defined. In order to rank TRIFNs, we have defi...

The quality of rankings can be evaluated by computing their correlation to an optimal ranking. State of the art ranking correlation coefficients like Kendall’s τ and Spearman’s ρ do not allow for the user to specify similarities between differing object classes and thus treat the transposition of objects from similar classes the same way as that of objects from dissimilar classes. We propose Cl...

We present a supervised learning to rank algorithm that effectively orders images by exploiting the structure in image sequences. Most often in the supervised learning to rank literature, ranking is approached either by analyzing pairs of images or by optimizing a list-wise surrogate loss function on full sequences. In this work we propose MidRank, which learns from moderately sized sub-sequenc...

Player ranking can be used to determine the quality of the contributions of a player to a collaborative community. However, collaborative games with no explicit objectives do not support player ranking, as there is no metric to measure the quality of player contributions. An implicit objective of such communities is not being disruptive towards other players. In this paper, we propose a paramet...

abstract. a practical common weight scalarizing function methodology with an improved discriminating power for technology selection is introduced. the proposed scalarizing function methodology enables the evaluation of the relative efficiency of decision-making units (dmus) with respect to multiple outputs and a single exact input with common weights. its robustness and discriminating power are...

We present a margin-based bound for ranking in a general setting, using the L∞ covering number of the hypothesis space as our complexity measure. Our bound suggests that ranking algorithms that maximize the ranking margin will generalize well. We produce a Smooth Margin Ranking algorithm, which is a modification of RankBoost analogous to Approximate Coordinate Ascent Boosting. We prove that thi...

A vertex k-ranking of a graph G is a function c : V (G) → {1, . . . , k} such that if c(u) = c(v), u, v ∈ V (G) then each path connecting vertices u and v contains a vertex w with c(w) > c(u). If each vertex v has a list of integers L(v) and for a vertex ranking c it holds c(v) ∈ L(v) for each v ∈ V (G) then c is called L-list k-ranking, where L = {L(v) : v ∈ V (G)}. In this paper we investigat...

Proving the termination of a flowchart program can be done by exhibiting a ranking function, i.e., a function from the program states to a wellfounded set, which strictly decreases at each program step. A standard method to automatically generate such a function is to compute invariants for each program point and to search for a ranking in a restricted class of functions that can be handled wit...

The Mass Volume (MV) curve is a visual tool to evaluate the performance of a scoring function with regard to its capacity to rank data in the same order as the underlying density function. Anomaly ranking refers to the unsupervised learning task which consists in building a scoring function, based on unlabeled data, with a MV curve as low as possible at any point. In this paper, it is proved th...