Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Feature and Instance Joint Selection: A Reinforcement Learning Perspective
Authors: Wei Fan, Kunpeng Liu, Hao Liu, Hengshu Zhu, Hui Xiong, Yanjie Fu
IJCAI 2022 | Venue PDF | 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. |