通过可解释性驱动的排名来获取主动功能
Osman Berke Guney1, Ketan Suhaas Saichandran2,3, Karim Elzokm1
1Department of Electrical & Computer Engineering, Boston University, MA, USA.
概括
本研究介绍了一种主动特征获取 (AFA) 框架,该框架可以动态选择机器学习模型的最有信息的特征. 这种以可解释性为导向的方法提高了数据采集的预测准确性和效率.
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:
- 获取完整的数据用于机器学习往往是不可行的,因为资源限制.
- 静态特征选择方法在特征重要性因实例而异时是不够的.
研究的目的:
- 开发一个主动特征采集 (AFA) 框架,用于动态,实例特定的特征选择.
- 提高机器学习模型的效率和在数据稀缺的情况下的预测准确性.
主要方法:
- 提出了一个使用本地解释技术的积极特征获取 (AFA) 框架.
- 将AFA重新定义为使用基于决策转换器的政策网络的特征预测任务.
- 训练政策网络以依据特定实例的重要性排名顺序获取特征.
主要成果:
- 与最先进的方法相比,拟议的AFA方法显示出更高的预测准确性.
- 实现了更高的功能采集效率,降低了数据采集成本和时间.
- 通过对多个不同的数据集进行广泛的实验来验证.
结论:
- 基于可解释性的AFA策略为高效的特征获取提供了一个有前途的解决方案.
- 动态,实例特定的特征选择对于优化机器学习在实际应用中至关重要.
- 开发的框架有效地解决了机器学习模型数据采集方面的挑战.
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