Enumerate Lasso Solutions for Feature Selection
Authors: Satoshi Hara, Takanori Maehara
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We confirm our theoretical results also in numerical simulations. Finally, in the gene expression and the text data, we demonstrate that the proposed method can enumerate a wide variety of meaningful feature sets, which are overlooked by the global optima. |
| Researcher Affiliation | Academia | Satoshi Hara,1,3 Takanori Maehara2,3 1. National Institute of Informatics, Tokyo, Japan 2. Shizuoka University, Shizuoka, Japan 3. JST, ERATO, Kawarabayashi Large Graph Project |
| Pseudocode | Yes | Algorithm 1 Enumeration algorithm |
| Open Source Code | Yes | 1The experiment codes are available at https://github.com/ sato9hara/Lasso Variants |
| Open Datasets | Yes | We used the thaliana gene expression data used in (Atwell et al. 2010). The 20 Newsgroups 2 is a dataset for text categorization. |
| Dataset Splits | No | The paper states: "we randomly split samples into 134 training samples and 33 test samples." and later "The training set comprised n = 1, 168 samples with p = 11, 648 words, whereas the test set consisted of n = 777 samples." It does not explicitly mention a separate validation split or dataset. |
| Hardware Specification | Yes | All experiments were conducted on 64-bit Cent OS 6.7 with an Intel Xeon E5-2670 2.6GHz CPU and 512GB RAM. |
| Software Dependencies | Yes | All codes were implemented in Python 3.5 with scikit-learn1. |
| Experiment Setup | Yes | For the training set, we applied Algorithm 1 with the regularization parameter ρ = 0.1n and the support parameter η = 0.05. Using Algorithm 1, we enumerated top-50 solutions with the regularization parameter ρ = 0.001n and the support parameter η = 4. |