Feature Selection Method Using Multi-Agent Reinforcement Learning Based on Guide Agents
In this study, we propose a method to automatically find features from a dataset that are effective for classification or prediction, using a new method called multi-agent reinforcement learning and a guide agent. Each feature of the dataset has one of the main and guide agents, and these agents dec...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/1/98 |
| _version_ | 1797431208827682816 |
|---|---|
| author | Minwoo Kim Jinhee Bae Bohyun Wang Hansol Ko Joon S. Lim |
| author_facet | Minwoo Kim Jinhee Bae Bohyun Wang Hansol Ko Joon S. Lim |
| author_sort | Minwoo Kim |
| collection | DOAJ |
| description | In this study, we propose a method to automatically find features from a dataset that are effective for classification or prediction, using a new method called multi-agent reinforcement learning and a guide agent. Each feature of the dataset has one of the main and guide agents, and these agents decide whether to select a feature. Main agents select the optimal features, and guide agents present the criteria for judging the main agents’ actions. After obtaining the main and guide rewards for the features selected by the agents, the main agent that behaves differently from the guide agent updates their Q-values by calculating the learning reward delivered to the main agents. The behavior comparison helps the main agent decide whether its own behavior is correct, without using other algorithms. After performing this process for each episode, the features are finally selected. The feature selection method proposed in this study uses multiple agents, reducing the number of actions each agent can perform and finding optimal features effectively and quickly. Finally, comparative experimental results on multiple datasets show that the proposed method can select effective features for classification and increase classification accuracy. |
| first_indexed | 2024-03-09T09:41:35Z |
| format | Article |
| id | doaj.art-4883930de37a4f95a01cf1aa3bde20f1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T09:41:35Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4883930de37a4f95a01cf1aa3bde20f12023-12-02T00:53:13ZengMDPI AGSensors1424-82202022-12-012319810.3390/s23010098Feature Selection Method Using Multi-Agent Reinforcement Learning Based on Guide AgentsMinwoo Kim0Jinhee Bae1Bohyun Wang2Hansol Ko3Joon S. Lim4Department of Computer Science, Gachon University, Sujeong-gu, Seongnam-si 13557, Gyeonggi-do, Republic of KoreaDepartment of Computer Science, University of Southern California, Los Angeles, CA 90007, USADepartment of Computer Science, Gachon University, Sujeong-gu, Seongnam-si 13557, Gyeonggi-do, Republic of KoreaDepartment of Computer Science, Gachon University, Sujeong-gu, Seongnam-si 13557, Gyeonggi-do, Republic of KoreaDepartment of Computer Science, Gachon University, Sujeong-gu, Seongnam-si 13557, Gyeonggi-do, Republic of KoreaIn this study, we propose a method to automatically find features from a dataset that are effective for classification or prediction, using a new method called multi-agent reinforcement learning and a guide agent. Each feature of the dataset has one of the main and guide agents, and these agents decide whether to select a feature. Main agents select the optimal features, and guide agents present the criteria for judging the main agents’ actions. After obtaining the main and guide rewards for the features selected by the agents, the main agent that behaves differently from the guide agent updates their Q-values by calculating the learning reward delivered to the main agents. The behavior comparison helps the main agent decide whether its own behavior is correct, without using other algorithms. After performing this process for each episode, the features are finally selected. The feature selection method proposed in this study uses multiple agents, reducing the number of actions each agent can perform and finding optimal features effectively and quickly. Finally, comparative experimental results on multiple datasets show that the proposed method can select effective features for classification and increase classification accuracy.https://www.mdpi.com/1424-8220/23/1/98feature selectionguide agentsmain agentsmulti-agentreinforcement learning (RL)rewards |
| spellingShingle | Minwoo Kim Jinhee Bae Bohyun Wang Hansol Ko Joon S. Lim Feature Selection Method Using Multi-Agent Reinforcement Learning Based on Guide Agents Sensors feature selection guide agents main agents multi-agent reinforcement learning (RL) rewards |
| title | Feature Selection Method Using Multi-Agent Reinforcement Learning Based on Guide Agents |
| title_full | Feature Selection Method Using Multi-Agent Reinforcement Learning Based on Guide Agents |
| title_fullStr | Feature Selection Method Using Multi-Agent Reinforcement Learning Based on Guide Agents |
| title_full_unstemmed | Feature Selection Method Using Multi-Agent Reinforcement Learning Based on Guide Agents |
| title_short | Feature Selection Method Using Multi-Agent Reinforcement Learning Based on Guide Agents |
| title_sort | feature selection method using multi agent reinforcement learning based on guide agents |
| topic | feature selection guide agents main agents multi-agent reinforcement learning (RL) rewards |
| url | https://www.mdpi.com/1424-8220/23/1/98 |
| work_keys_str_mv | AT minwookim featureselectionmethodusingmultiagentreinforcementlearningbasedonguideagents AT jinheebae featureselectionmethodusingmultiagentreinforcementlearningbasedonguideagents AT bohyunwang featureselectionmethodusingmultiagentreinforcementlearningbasedonguideagents AT hansolko featureselectionmethodusingmultiagentreinforcementlearningbasedonguideagents AT joonslim featureselectionmethodusingmultiagentreinforcementlearningbasedonguideagents |