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Feature Selection for Regression Based on Gamma Test Nested Monte Carlo Tree Search
| Content Provider | MDPI |
|---|---|
| Author | Li, Ying Li, Guohe Guo, Lingun |
| Copyright Year | 2021 |
| Description | This paper investigates the nested Monte Carlo tree search (NMCTS) for feature selection on regression tasks. NMCTS starts out with an empty subset and uses search results of lower nesting level simulation. Level 0 is based on random moves until the path reaches the leaf node. In order to accomplish feature selection on the regression task, the Gamma test is introduced to play the role of the reward function at the end of the simulation. The concept Vratio of the Gamma test is also combined with the original UCT-tuned1 and the design of stopping conditions in the selection and simulation phases. The proposed GNMCTS method was tested on seven numeric datasets and compared with six other feature selection methods. It shows better performance than the vanilla MCTS framework and maintains the relevant information in the original feature space. The experimental results demonstrate that GNMCTS is a robust and effective tool for feature selection. It can accomplish the task well in a reasonable computation budget. |
| Starting Page | 1331 |
| e-ISSN | 10994300 |
| DOI | 10.3390/e23101331 |
| Journal | Entropy |
| Issue Number | 10 |
| Volume Number | 23 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-10-12 |
| Access Restriction | Open |
| Subject Keyword | Entropy Information and Library Science Feature Selection Regression Nested Monte Carlo Tree Search (nmcts) Filter Gamma Test Gnmcts |
| Content Type | Text |
| Resource Type | Article |
