Name
Abstract | ||
|---|---|---|
In last years - especially due to the development of telecommunications - fairness modelling has received a strong attention. This article presents an approach for categorizing unknown relations according to their "closeness" to known relations. We consider as reference relations, the well-known: Pareto dominance, Leximin and Proportional fairness relation. We simulate each relation generating a learning dataset that is used for learning Neural Networks. The learning performance evaluation is based in several metrics, which are used as a "signature" of each relation. Besides, we develop a new function that gives an estimation about the "closeness" between relations. This concept permits us to categorise a new dataset (generated by an unknown relation) according its "closeness" with the Pareto dominance, Leximin and Proportional fairness relations know relations. Our experimental results are coherent with the alpha fairness concept. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/SMC.2015.365 | 2015 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2015): BIG DATA ANALYTICS FOR HUMAN-CENTRIC SYSTEMS |
Keywords | Field | DocType |
Fairness, Maxmin Fairness, Priority Fairness, Neural Networks, Supervised Learning | Resource management,Numerical models,Computer science,Closeness,Artificial intelligence,Artificial neural network,Pareto principle,Machine learning | Conference |
ISSN | Citations | PageRank |
1062-922X | 0 | 0.34 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sebastián Basterrech | 1 | 35 | 9.38 |
| Kei Ohnishi | 2 | 39 | 17.71 |
| Mario Köppen | 3 | 1405 | 166.06 |