Feature and Instance Joint Selection: A Reinforcement Learning Perspective
Authors: Wei Fan, Kunpeng Liu, Hao Liu, Hengshu Zhu, Hui Xiong, Yanjie Fu
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, extensive experiments on real-world datasets have demonstrated the improved performances. |
| Researcher Affiliation | Collaboration | 1University of Central Florida 2Portland State University 3Hong Kong University of Science and Technology 4Baidu Talent Intelligence Center 5Rutgers University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We use four public datasets of different domains on classification task to validate our methods: Forest Cover (FC) dataset is a publicly available dataset from Kaggle1 including characteristics of wilderness areas. Madelon dataset is a Nips 2003 workshop dataset containing data points grouped in 32 clusters and labeled by 1 and -1 [Dua and Graff, 2017]. Spam dataset is a collection of spam emails [Dua and Graff, 2017]. USPS dataset is a handwritten digit database including handwritten digit images [Cai et al., 2010]. |
| Dataset Splits | Yes | We randomly split the data into train data (70%) and test data (30%) where categorical features are encoded in one-hot. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | The size of memory unit is set to 300 in experience replay. For the reward measurement, we consider accuracy, relevance score and redundancy score following [Liu et al., 2019]. The policy networks are set as two fully-connected layers of 512 middle states with Re LU as activation function. In RL exploration, the discount factor γ is set to 0.9, and we use ϵ-greedy exploration with ϵ as 0.8. |