Human-in-the-Loop Feature Selection
Authors: Alvaro H. C. Correia, Freddy Lecue2438-2445
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our method in two scenarios: (i) a proofof-concept image classification task and (ii) a real-world project risk classification task. |
| Researcher Affiliation | Collaboration | Alvaro H. C. Correia ENSTA Paris Tech, Palaiseau, France Utrecht University, The Netherlands a.h.chaimcorreia@uu.nl Freddy Lecue Cort AIx Thales, Montreal, Canada Inria, Sophia Antipolis, France freddy.lecue@inria.fr |
| Pseudocode | No | The paper describes mathematical formulations and processes but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code for the image classification task can be found at github.com/Al Correia/ Human-in-the-loop-Feature-Selection. |
| Open Datasets | Yes | We tested our model on an augmented MNIST dataset (Le Cun et al. 1998) composed of cluttered images as in (Mnih et al. 2014). |
| Dataset Splits | Yes | Training, testing and validation sets correspond to 60/20/20% of the projects, respectively, with each class equally distributed in the validation and test sets. |
| Hardware Specification | Yes | All experiments were developed on top of the Tensorflow python API (Google Research 2015) and run on a single GPU Nvidia GEFORCE GTX 1080 Ti. |
| Software Dependencies | No | The paper mentions 'Tensorflow python API (Google Research 2015)' but does not provide a specific version number for TensorFlow or any other software libraries used. |
| Experiment Setup | Yes | The model based on the PD estimator was trained for 100 epochs with batches of 876 projects (1000 images)... the whole model was updated via Adagrad (Duchi, Hazan, and Singer 2011) with initial learning rate of .5. The hyperparameter λ in (7) was set to 1... For the SF estimator, the regularization parameters φ, λs and λv in (9) and (10) were set to .2, 1 and 1, respectively. For the PD estimator... it was set to 10 and decayed by 4% every 100 steps. |