Feature Importance Ranking for Deep Learning
Authors: Maksymilian Wojtas, Ke Chen
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A thorough evaluation on synthetic, benchmark and real data sets suggests that our approach outperforms several state-of-the-art feature importance ranking and supervised feature selection methods. |
| Researcher Affiliation | Academia | Maksymilian A. Wojtas Ke Chen Department of Computer Science, The University of Manchester, Manchester M13 9PL, U.K. {maksymilian.wojtas,ke.chen}@manchester.ac.uk |
| Pseudocode | Yes | while the pseudo code can be found from Sect. D in supplementary materials. |
| Open Source Code | Yes | Our source code is available: https://github.com/maksym33/Feature Importance DL |
| Open Datasets | Yes | Our first evaluation employs 3 synthetic datasets in literature [17, 11] for feature selection regarding regression and binary/multiclass classification... MNIST Dataset [21]... glass [22], vowel [22], TOX-171 [23] and yale [24]... GM12878 cell line (200 dp)... |
| Dataset Splits | Yes | we always use 5-fold cross-validation for evaluation and report the performance statistics, i.e., mean and standard deviation estimated on 5 folds. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions types of models used (e.g., MLP, CNN, kernel SVMs) but does not provide specific software library names with version numbers (e.g., PyTorch 1.x, TensorFlow 2.x, scikit-learn 0.x). |
| Experiment Setup | No | The paper states 'the details of all the experimental settings can be found from Sect. A in Supplementary Materials,' but the main text itself does not include specific hyperparameters or system-level training settings. |