Comparison of Inference Relations Defined over Different Sets of Ranking Functions

2017 ◽  
pp. 225-235 ◽  
Author(s):  
Christoph Beierle ◽  
Steven Kutsch
Keyword(s):  
Ranking Functions

scholarly journals Synthesis of ranking functions using extremal counterexamples

2015 ◽  
Vol 50 (6) ◽  
pp. 608-618 ◽  
Author(s):  
Laure Gonnord ◽  
David Monniaux ◽  
Gabriel Radanne
Keyword(s):  
Ranking Functions

Explaining monotonic ranking functions

2020 ◽  
Vol 14 (4) ◽  
pp. 640-652
Author(s):  
Abraham Gale ◽  
Amélie Marian
Keyword(s):  
Decision Making ◽  
Selection Process ◽  
A Priori ◽  
Data Distribution ◽  
Synthetic Data ◽  
Decision Processes ◽  
Decision Makers ◽  
Ranking Functions ◽  
High School Admissions ◽  
Parameter Values

Ranking functions are commonly used to assist in decision-making in a wide variety of applications. As the general public realizes the significant societal impacts of the widespread use of algorithms in decision-making, there has been a push towards explainability and transparency in decision processes and results, as well as demands to justify the fairness of the processes. In this paper, we focus on providing metrics towards explainability and transparency of ranking functions, with a focus towards making the ranking process understandable, a priori , so that decision-makers can make informed choices when designing their ranking selection process. We propose transparent participation metrics to clarify the ranking process, by assessing the contribution of each parameter used in the ranking function in the creation of the final ranked outcome, using information about the ranking functions themselves, as well as observations of the underlying distributions of the parameter values involved in the ranking. To evaluate the outcome of the ranking process, we propose diversity and disparity metrics to measure how similar the selected objects are to each other, and to the underlying data distribution. We evaluate the behavior of our metrics on synthetic data, as well as on data and ranking functions on two real-world scenarios: high school admissions and decathlon scoring.

Optimization of knowledge transferring costs in designing product portfolio: a fuzzy binary linear programming model

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nahid Dorostkar-Ahmadi ◽  
Mohsen Shafiei Nikabadi ◽  
Saman babaie-kafaki
Keyword(s):  
Linear Programming ◽  
Programming Model ◽  
Fuzzy Model ◽  
Linear Programming Model ◽  
Product Portfolio ◽  
Ranking Functions ◽  
Content Type ◽  
Binary Linear Programming ◽  
Knowledge Based ◽  
Knowledge Based Economy

Purpose The success of any organization in a knowledge-based economy depends on effective knowledge transferring and then proper use of the transferred knowledge. As is known, optimizing the knowledge transferring costs in a product portfolio plays an important role in improving productivity, competitive advantage and profitability of any organization. Therefore, this paper aims to determine an optimal product portfolio by minimizing the konlwedge transferring costs. Design/methodology/approach Here, a fuzzy binary linear programming model is used to select an optimal product portfolio. The model is capable of considering the knowledge transferring costs while taking into account the human-hours constraints for each product by a fuzzy approach. Using fuzzy ranking functions, a reasonable solution of the model can be achieved by classical or metaheuristic algorithms. Findings Numerical experiments indicate that the proposed fuzzy model is practically effective. Originality/value The contributions of this work essentially consist of considering knowledge transferring costs in selecting an optimal product portfolio and using the fuzzy data which make the model more realistic.

Generating exact nonlinear ranking functions by symbolic-numeric hybrid method

2013 ◽  
Vol 26 (2) ◽  
pp. 291-301 ◽  
Author(s):  
Liyong Shen ◽  
Min Wu ◽  
Zhengfeng Yang ◽  
Zhenbing Zeng
Keyword(s):  
Hybrid Method ◽  
Ranking Functions

A Novel Ranking Approach to Solving Fully LR-Intuitionistic Fuzzy Transportation Problems

2018 ◽  
Vol 15 (01) ◽  
pp. 95-112 ◽  
Author(s):  
Abhishekh ◽  
A. K. Nishad
Keyword(s):  
Transportation Problem ◽  
Fuzzy Numbers ◽  
Optimal Solution ◽  
Solution Procedure ◽  
Ranking Functions ◽  
Transportation Problems ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Fuzzy Transportation Problem

To the extent of our knowledge, there is no method in fuzzy environment to solving the fully LR-intuitionistic fuzzy transportation problems (LR-IFTPs) in which all the parameters are represented by LR-intuitionistic fuzzy numbers (LR-IFNs). In this paper, a novel ranking function is proposed to finding an optimal solution of fully LR-intuitionistic fuzzy transportation problem by using the distance minimizer of two LR-IFNs. It is shown that the proposed ranking method for LR-intuitionistic fuzzy numbers satisfies the general axioms of ranking functions. Further, we have applied ranking approach to solve an LR-intuitionistic fuzzy transportation problem in which all the parameters (supply, cost and demand) are transformed into LR-intuitionistic fuzzy numbers. The proposed method is illustrated with a numerical example to show the solution procedure and to demonstrate the efficiency of the proposed method by comparison with some existing ranking methods available in the literature.

Genetic Programming-Based Discovery of Ranking Functions for Effective Web Search

2005 ◽  
Vol 21 (4) ◽  
pp. 37-56 ◽  
Author(s):  
WEIGUO FAN ◽  
MICHAEL D. GORDON ◽  
PRAVEEN PATHAK ◽  
PRAVEEN PATHAK
Keyword(s):  
Genetic Programming ◽  
Web Search ◽  
Ranking Functions
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