Neural Representation and Learning of Hierarchical 2-additive Choquet Integrals
Authors: Roman Bresson, Johanne Cohen, Eyke Hüllermeier, Christophe Labreuche, Michèle Sebag
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The empirical validation of NEUR-HCI on real-world and artificial benchmarks demonstrates the merits of the approach compared to state-of-art baselines. This section reports on the empirical performance of NEUR-HCI comparatively to the state of the art. |
| Researcher Affiliation | Collaboration | 1 Thales Research and Technology, 91767 Palaiseau, France 2 LRI, CNRS INRIA, Universit e Paris-Saclay, 91400 Orsay, France 3 Department of Computer Science, Paderborn University, 33098 Paderborn, Germany |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It describes the neural network architecture and its components in narrative text and diagrams (Figures 2 and 3). |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It does not include a specific repository link or an explicit code release statement. |
| Open Datasets | Yes | The standard MCDM benchmarks include CPU, CEV, LEV, MPG, Den Bosch (DB), Mammographics (MG), Journal8, Boston Housing9, Titanic10 and the Dagstuhl-15512 Arguments Quality corpus11 [Wachsmuth et al., 2017]. The last one, reporting the preferences of three decision makers, yields three sub-datasets referred to as Arguments 1, Arguments 2, Arguments 3 (each one being associated with a sin-gle decision maker). Footnotes provide URLs for: 1Dataset: https://archive.ics.uci.edu/ml/datasets/car+evaluation, 8https://cs.uni-paderborn.de/?id=63916, 9http://lib.stat.cmu.edu/datasets/boston, 10https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/ problem12.html, 11http://argumentation.bplaced.net/arguana/data |
| Dataset Splits | Yes | Each dataset is randomly split into an 80% train and 20% test sets; the performance of the model trained from the train set is measured on the test set, and averaged over 1,000 random splits. |
| Hardware Specification | Yes | The MLP and NEUR-HCI computational costs are below 5 minutes for each dataset on an Intel i7. |
| Software Dependencies | No | The paper mentions 'Matlab implementation' for CUR but does not provide specific version numbers for any software dependencies or libraries used for NEUR-HCI, such as neural network frameworks or programming language versions. |
| Experiment Setup | Yes | NEUR-HCI hyper-parameters include the regularization weight K, set to 0 after a few preliminary experiments. The actual number of sigmoids is minimized through L1 regularization, Eq. 10. Multilayer perceptron (MLP) with 1 fully connected hidden layer of n2 neurons, sigmoid activation function. |