Incremental Elicitation of Rank-Dependent Aggregation Functions based on Bayesian Linear Regression
Authors: Nadjet Bourdache, Patrice Perny, Olivier Spanjaard
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present numerical tests showing the interest of the proposed approach. and 5 Numerical Tests Before coming to the results of numerical tests2 we conducted on randomly generated instances to evaluate the behavior of Algorithm 1 |
| Researcher Affiliation | Academia | Nadjet Bourdache , Patrice Perny and Olivier Spanjaard Sorbonne Universit e, CNRS, LIP6, F-75005 Paris, France |
| Pseudocode | Yes | Algorithm 1 Incremental decision making and Algorithm 2 Approximating density function p(w|y(i)) |
| Open Source Code | No | No explicit statement or link for open-source code specific to the methodology described in this paper was found. |
| Open Datasets | No | We generate instances with 5 criteria and 100 Pareto optimal alternatives. Every alternative a in each generated set A is drawn as follows: a first vector v of size p 1 is uniformly drawn in [0, 1]p 1, then a is obtained by setting ai =v(i) v(i 1) for i=1, . . . , p, where v(0) =0 and v(p) =1. |
| Dataset Splits | No | No explicit mention of training, validation, or test dataset splits was found. |
| Hardware Specification | Yes | running on an Intel(R) Core(TM) i7-4790 CPU with 15GB of RAM. |
| Software Dependencies | No | Implementation in Python using the tmvtnorm R s library to draw vectors according to multivariate truncated normal densities (Specific version numbers for Python or the tmvtnorm library are not provided.) |
| Experiment Setup | Yes | Assume that Algorithm 1 is launched with an acceptance threshold δ = 0.02. For the elicitation of OWA parameters, the prior is set to N((10, . . . , 10), 100I), where I is the identity matrix. For Choquet parameters, the prior is set to N(µ, 100I), where µi =10 if |Yi| = 1 and µi =0 otherwise and To simulate the DM s answers, we use the model given in Equation 4 with ε(i) N(0, σ2) for σ {0, 0.1, 0.2} |