Summary of Enhancing Classification Performance Via Reinforcement Learning For Feature Selection, by Younes Ghazagh Jahed et al.
Enhancing Classification Performance via Reinforcement Learning for Feature Selection
by Younes Ghazagh Jahed, Seyyed Ali Sadat Tavana
First submitted to arxiv on: 9 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the role of effective feature selection in enhancing the performance of classification models using reinforcement learning (RL) algorithms like Q-learning (QL) and SARSA learning. By employing these methods on the Breast Cancer Coimbra dataset (BCCDS) with three normalization techniques, the study evaluates their impact on model accuracy. The results demonstrate that QL@Min-Max and SARSA@l2 achieve the highest classification accuracies of 87% and 88%, respectively, highlighting the effectiveness of RL-based feature selection methods in optimizing classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make machine learning models better by choosing the right features. They used special algorithms called Q-learning (QL) and SARSA learning to do this. The algorithms were tested on a big dataset about breast cancer and they worked really well. The best results came from using one of these algorithms with a certain way of making numbers normal, which was great for getting accurate predictions. |
Keywords
* Artificial intelligence * Classification * Feature selection * Machine learning * Reinforcement learning
