模糊方法的应用,用于评估和选择相关的对象,特征及其范围
1Institute of Computer Science, College of Natural Sciences, University of Rzeszów, Rejtana Str. 16C, 35-959 Rzeszów, Poland.
Entropy (Basel, Switzerland)
|August 26, 2023
概括
本研究介绍了在机器学习中用于特征选择和对象子集选择的基于模糊的新算法. 这些方法简化了复杂的数据集,减少了维度,提高了计算效率,显示出超越传统技术的前景.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 功能选择对于简化机器学习问题,减少维度和提高计算速度至关重要.
- 现有的方法可能并不总是有效地识别最相关的属性或对象.
研究的目的:
- 为数据集中的相关属性和对象选择提出新的算法.
- 在特征和对象选择中评估模糊方法的有效性.
- 为了确定所选属性和对象的最佳值范围.
主要方法:
- 基于特征和对象选择的模糊方法开发新的算法.
- 在索纳数据集上应用和评估拟议的方法.
- 模糊方法与传统特征选择技术的比较.
主要成果:
- 初步结果表明,选择相关属性,对象及其值范围的新方法.
- 对索纳数据集的详细分析表明,拟议的基于模糊的算法的积极影响.
- 该方法显示了识别真正相关属性的子集的潜力,超过了一些传统方法.
结论:
- 拟议的基于模糊的算法为相关属性和对象选择提供了有效的方法.
- 这种方法有助于简化机器学习问题,提高计算效率.
- 研究结果表明,与传统方法相比,模糊方法在识别真正相关特征方面具有优越性.
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