A GA-based algorithm meets the fair ranking problem

2021 ◽  
Vol 58 (6) ◽  
pp. 102711
Author(s):  
Saedeh Tahery ◽  
Seyyede Zahra Aftabi ◽  
Saeed Farzi
Keyword(s):  
Ranking Problem ◽  
Fair Ranking

OPTIMAL TREE RANKING IS IN $\mathcal{NC}$

1992 ◽  
Vol 02 (01) ◽  
pp. 31-41 ◽  
Author(s):  
PILAR DE LA TORRE ◽  
RAYMOND GREENLAW ◽  
TERESA M. PRZYTYCKA
Keyword(s):  
General Problem ◽  
Sequential Algorithm ◽  
Natural Numbers ◽  
Ranking Problem ◽  
Crew Pram ◽  
Optimal Tree ◽  
Optimal Ranking

This paper places the optimal tree ranking problem in [Formula: see text]. A ranking is a labeling of the nodes with natural numbers such that if nodes u and v have the same label then there exists another node with a greater label on the path between them. An optimal ranking is a ranking in which the largest label assigned to any node is as small as possible among all rankings. An O(n) sequential algorithm is known. Researchers have speculated that this problem is P-complete. We show that for an n-node tree, one can compute an optimal ranking in O( log n) time using n2/ log n CREW PRAM processors. In fact, our ranking is super critical in that the label assigned to each node is absolutely as small as possible. We achieve these results by showing that a more general problem, which we call the super critical numbering problem, is in [Formula: see text]. No [Formula: see text] algorithm for the super critical tree ranking problem, approximate or otherwise, was previously known; the only known [Formula: see text] algorithm for optimal tree ranking was an approximate one.

scholarly journals On the linear ranking problem for integer linear-constraint loops

2013 ◽  
Vol 48 (1) ◽  
pp. 51-62 ◽  
Author(s):  
Amir M. Ben-Amram ◽  
Samir Genaim
Keyword(s):  
Linear Constraint ◽  
Ranking Problem

scholarly journals Matching anticancer compounds and tumor cell lines by neural networks with ranking loss

2022 ◽  
Vol 4 (1) ◽  
Author(s):  
Paul Prasse ◽  
Pascal Iversen ◽  
Matthias Lienhard ◽  
Kristina Thedinga ◽  
Chris Bauer ◽  
...  
Keyword(s):  
Tumor Cell ◽  
Cell Line ◽  
Cancer Cell ◽  
Drug Sensitivity ◽  
Inhibitory Concentration ◽  
Cancer Cell Line ◽  
Tumor Cell Line ◽  
Ranking Problem ◽  
Ranking Loss

ABSTRACT Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drug components that are likely to achieve the highest efficacy for a cancer cell line at hand at a therapeutic dose. State of the art drug sensitivity models use regression techniques to predict the inhibitory concentration of a drug for a tumor cell line. This regression objective is not directly aligned with either of these principal goals of drug sensitivity models: We argue that drug sensitivity modeling should be seen as a ranking problem with an optimization criterion that quantifies a drug’s inhibitory capacity for the cancer cell line at hand relative to its toxicity for healthy cells. We derive an extension to the well-established drug sensitivity regression model PaccMann that employs a ranking loss and focuses on the ratio of inhibitory concentration and therapeutic dosage range. We find that the ranking extension significantly enhances the model’s capability to identify the most effective anticancer drugs for unseen tumor cell profiles based in on in-vitro data.

scholarly journals The concept of stochastic dominance in ranking investment alternatives

2005 ◽  
Vol 50 (164) ◽  
pp. 135-149
Author(s):  
Dejan Trifunovic
Keyword(s):  
Stochastic Dominance ◽  
Distribution Functions ◽  
Third Order ◽  
Ranking Problem ◽  
First Order ◽  
Order Stochastic Dominance ◽  
Arbitrage Opportunities ◽  
Second Order Stochastic Dominance ◽  
Mean Variance ◽  
Almost Stochastic Dominance

In order to rank investments under uncertainty, the most widely used method is mean variance analysis. Stochastic dominance is an alternative concept which ranks investments by using the whole distribution function. There exist three models: first-order stochastic dominance is used when the distribution functions do not intersect, second-order stochastic dominance is applied to situations where the distribution functions intersect only once, while third-order stochastic dominance solves the ranking problem in the case of double intersection. Almost stochastic dominance is a special model. Finally we show that the existence of arbitrage opportunities implies the existence of stochastic dominance, while the reverse does not hold.

scholarly journals Identifying important nodes from content-associated heterogeneous graph by LeaderRank

2021 ◽  
Vol 2113 (1) ◽  
pp. 012082
Author(s):  
Yulong Dai ◽  
Qiyou Shen ◽  
Xiangqian Xu ◽  
Jun Yang
Keyword(s):  
Real World ◽  
Experimental Analysis ◽  
Differential Information ◽  
World Systems ◽  
Ranking Problem ◽  
Data Set ◽  
Importance Ranking ◽  
Basic Concepts ◽  
Important Nodes ◽  
Graph Nodes

Abstract Most real-world systems consist of a large number of interacting entities of many types. However, most of the current researches on systems are based on the assumption that the type of node or link in the network is unique. In other words, the network is homogeneous, containing the same type of nodes and links. Based on this assumption, differential information between nodes and edges is ignored. This paper firstly introduces the research background, challenges and significance of this research. Secondly, the basic concepts of the model are introduced. Thirdly, a novel type-sensitive LeaderRank algorithm is proposed and combined with distance rule to solve the importance ranking problem of content-associated heterogeneous graph nodes. Finally, the writer influence data set is used for experimental analysis to further prove the validity of the model.

scholarly journals Modified VIKOR method as a component of decision support of information technology of the dual form of education

2021 ◽  
Vol 102 (2) ◽  
pp. 121-129
Author(s):  
Taras Lechachenko ◽  
Olena Karelina
Keyword(s):  
Decision Support ◽  
Educational Institution ◽  
Decision Support Tool ◽  
Optimization Method ◽  
Educational Process ◽  
Vikor Method ◽  
Ranking Problem ◽  
Support Tool ◽  
Lack Of Information ◽  
Proposed Modification

The model for supporting the student decision in choosing the subjects of specialty educational program based on VIKOR multi-criteria optimization method is developed in this paper. The developed model is the component of the dual education information system (when the student is trained in the company and educational institution at the same time on the basis of the contract). This component is a decision support tool for a student training by a dual education, taking into account the expert opinion of stakeholders in the learning process. The criteria of dual education stakeholders for ranking alternatives (subjects of the specialty program): student, educational institution, company are outlined. VIKOR method is modified by the selection of subsystems criteria in order to derive an integrated assessment of experts from different subsystems. The algorithm for integrating ratings of ranking subsystems is developed, taking into account the strategy of maximum group usefulness of VIKOR method. The weighting coefficients of subsystems and their criteria are determined by T. Saati method of hierarchies analysis. In order to take into account the uncertainty associated with the lack of information, intuitionistic fuzzy sets are used to assign assessments of the alternatives ranking by subsystem experts. The proposed modification of VIKOR method makes it possible to rank the alternatives with the involvement of different specialists with their own criteria system. This approach increases the accuracy of the obtained results, as the criteria are further divided into holders subsystems of the ranking problem. This approach enables to carry out deeper and broader analysis of ranking problem aspects. Numerical example of the developed model which confirms the acceptability of its application in practice in the dual educational process application is illustrated in this paper.

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