Dominance and Optimisation Based on Scale-Invariant Maximum Margin Preference Learning
Authors: Mojtaba Montazery, Nic Wilson
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we compare the relations and their associated optimality sets based on their decisiveness, computation time and cardinality of the optimal set. We also discuss connections with imprecise probability. |
| Researcher Affiliation | Academia | Mojtaba Montazery and Nic Wilson Insight Centre for Data Analytics School of Computer Science and IT University College Cork, Ireland {mojtaba.montazery, nic.wilson}@insight-centre.org |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. Computational methods are described in textual and mathematical form. |
| Open Source Code | No | The paper does not provide a link to or explicitly state that the source code for its methodology is available. |
| Open Datasets | Yes | The experiments make use of a subset of a year s worth of real ridesharing records. These were provided by a commercial ridesharing system Carma (see http://gocarma.com/). We base our experiments on 13 benchmarks derived from this data set. |
| Dataset Splits | No | The paper does not specify training, validation, or test dataset splits. It describes how data for generating decisive pairs and optimal solutions was created but not as standard splits. |
| Hardware Specification | Yes | To conduct the experiments, CPLEX 12.6.3 is used as the solver on a computer facilitated by an Intel Xeon E312xx 2.20 GHz processor and 8 GB RAM memory. |
| Software Dependencies | Yes | To conduct the experiments, CPLEX 12.6.3 is used as the solver on a computer facilitated by an Intel Xeon E312xx 2.20 GHz processor and 8 GB RAM memory. |
| Experiment Setup | Yes | Here, we would like to examine how decisive each relation is, i.e., which relation is weaker and by how much. We randomly generate 1000 pairs (α, β), based on a uniform distribution for each feature. A pair (α, β) is called decisive for a preference relation if one of them can (strictly) dominate the other one; for example, the pair (α, β) is decisive for I Λ if and only if α I Λ β or β I Λ α. This is iff either (α I Λ β and β I Λ α) or (β I Λ α and α I Λ β). We also consider another relation I F Λ which is the intersection of I Λ and F Λ, so that α I F Λ β α I Λ β and α F Λ β. |