AFS: An Attention-Based Mechanism for Supervised Feature Selection
Authors: Ning Gui, Danni Ge, Ziyin Hu3705-3713
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that AFS achieves the best accuracy and stability in comparison to several state-of-art feature selection algorithms upon both MNIST, noisy MNIST and several datasets with small samples. A set of experiments are designed on both Large-dimensionality Small-instance dataset (denoted as L/S dataset) and Medium/large-dimensionality Large-instance dataset (short for M/L dataset). |
| Researcher Affiliation | Academia | 1School of Software, Central South University, China, ninggui@gmail.com 2 School of Informatics, Zhejiang Sci-Tech University, China, dongfangdecheng@gmail.com, fayssica@gmail.com |
| Pseudocode | No | The paper describes algorithms and includes mathematical equations but does not present a structured pseudocode block or algorithm labeled explicitly as such. |
| Open Source Code | Yes | Source code at https://github.com/upup123/AAAI-2019-AFS. |
| Open Datasets | Yes | The MNIST dataset2 consists of greyscale thumbnails, 28 x 28 pixels, of handwritten digits 0 to 9. The n-MNIST dataset3 consists of three MNIST variants... The Lung_discrete and Isolet4 are L/S datasets. 2 http://yann.lecun.com/exdb/mnist/ 3 http://csc.lsu.edu/~saikat/n-mnist/ 4 http://featureselection.asu.edu |
| Dataset Splits | Yes | For L/S datasets, 3 times 3 fold cross-validation is adopted to provide a fair comparison. |
| Hardware Specification | No | The paper does not specify the hardware used for experiments (e.g., specific CPU/GPU models, memory, or cluster configurations). |
| Software Dependencies | No | The paper mentions "scikit-feature selection repository" but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Model parameters are initialized with truncated normal distribution with a mean of 0 and standard deviation of 0.1. The model is optimized by Adam, with batch size 100. The weight of regularizer is 0.0001. For the solution of AFS and Roy, the training step is both set to 3000 as they both begin to converge. |