Enhancing Classification Performance via Reinforcement Learning for Feature Selection
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913260514574336 |
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| author | Jahed, Younes Ghazagh Tavana, Seyyed Ali Sadat |
| author_facet | Jahed, Younes Ghazagh Tavana, Seyyed Ali Sadat |
| contents | Feature selection plays a crucial role in improving predictive accuracy by identifying relevant features while filtering out irrelevant ones. This study investigates the importance of effective feature selection in enhancing the performance of classification models. By employing reinforcement learning (RL) algorithms, specifically Q-learning (QL) and SARSA learning, this paper addresses the feature selection challenge. Using the Breast Cancer Coimbra dataset (BCCDS) and three normalization methods (Min-Max, l1, and l2), the study evaluates the performance of these algorithms. Results show that QL@Min-Max and SARSA@l2 achieve the highest classification accuracies, reaching 87% and 88%, respectively. This highlights the effectiveness of RL-based feature selection methods in optimizing classification tasks, contributing to improved model accuracy and efficiency. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_05979 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Classification Performance via Reinforcement Learning for Feature Selection Jahed, Younes Ghazagh Tavana, Seyyed Ali Sadat Machine Learning 68T99 E.1 Feature selection plays a crucial role in improving predictive accuracy by identifying relevant features while filtering out irrelevant ones. This study investigates the importance of effective feature selection in enhancing the performance of classification models. By employing reinforcement learning (RL) algorithms, specifically Q-learning (QL) and SARSA learning, this paper addresses the feature selection challenge. Using the Breast Cancer Coimbra dataset (BCCDS) and three normalization methods (Min-Max, l1, and l2), the study evaluates the performance of these algorithms. Results show that QL@Min-Max and SARSA@l2 achieve the highest classification accuracies, reaching 87% and 88%, respectively. This highlights the effectiveness of RL-based feature selection methods in optimizing classification tasks, contributing to improved model accuracy and efficiency. |
| title | Enhancing Classification Performance via Reinforcement Learning for Feature Selection |
| topic | Machine Learning 68T99 E.1 |
| url | https://arxiv.org/abs/2403.05979 |