Variable Selection via Penalized Neural Network: a Drop-Out-One Loss Approach
Authors: Mao Ye, Yan Sun
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on simulated and real world datasets show the efficiency of our method in terms of variable selection and prediction accuracy. |
| Researcher Affiliation | Academia | 1Department of Statistics, Purdue University, West Lafayette, IN, USA. Correspondence to: Mao Ye <ye207@purdue.edu>. |
| Pseudocode | Yes | Algorithm 1 Training Penalized Neural Network; Algorithm 2 Greedy Elimination Method |
| Open Source Code | No | The paper mentions 'Details on implementation for all experiments are in the supplementary material.' but does not explicitly state that source code for their methodology is provided or offer a specific link. |
| Open Datasets | Yes | CCLE is taken from (Liang et al., 2017) and the other 3 datasets are from UCI machine learning repository. |
| Dataset Splits | Yes | Each dataset consists of 600 observations, with 200 for training, 100 for validation and 300 for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running experiments. |
| Software Dependencies | No | The paper mentions software components like 'tanh as the activation function' and refers to algorithms like 'GIST algorithm' and 'block-wise descent algorithm', but it does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | The network structure of Spinn and GEPNN are set to have 6 hidden units. We set the number of hidden units of Spinn and GEPNN to be 3 for CCLE, CCPP and Airfoil. Since we add nonlinear features for Boston Housing dataset, we reduce the number of hidden units to 2 for Spinn and GEPNN. We need to tune λ0, α, λ1 and thre(t) for the algorithm. |