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Interactive machine learning for health informatics: when do we need the human-in-the-loop?
Brain Informatics Pub Date : 2016-10-18 , DOI: 10.1007/s40708-016-0042-6
Andreas Holzinger 1, 2
Affiliation  

Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as "algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human." This "human-in-the-loop" can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.

中文翻译:

用于健康信息学的交互式机器学习:我们什么时候需要“环环相扣”的人?

机器学习(ML)是计算机科学中增长最快的领域,而健康信息学则是最大的挑战之一。ML的目标是开发可以随着时间的推移而学习和改进并可以用于预测的算法。大多数机器学习研究人员专注于自动机器学习(aML),在语音识别,推荐系统或自动驾驶汽车等方面已经取得了长足的进步。自动方法极大地受益于具有大量训练集的大数据。但是,在健康领域,有时我们会遇到少量数据集或罕见事件,其中aML方法遭受的训练样本不足。在这里,交互式机器学习(iML)可能会有所帮助,其源于强化学习,偏好学习和主动学习。术语iML尚未得到很好的使用,因此我们将其定义为“可以与代理互动的算法,并且可以通过这些互动优化代理的学习行为,其中代理也可以是人类。” 这种“在圈中的人”可以有益于解决计算难题,例如子空间聚类,蛋白质折叠或健康数据的k匿名化,其中人类的专业知识可以通过启发式选择来减少指数搜索空间样品。因此,否则将是NP难题,通过参与学习阶段的人员的输入和协助,极大地降低了复杂性。在解决计算困难的问题(例如子空间聚类,蛋白质折叠或健康数据的k匿名化)方面可能会有所帮助,其中人类的专业知识可以通过启发式选择样本来帮助减少指数搜索空间。因此,否则将是NP难题,通过参与学习阶段的人员的输入和协助,极大地降低了复杂性。在解决计算困难的问题(例如子空间聚类,蛋白质折叠或健康数据的k匿名化)方面可能会有所帮助,其中人类的专业知识可以通过启发式选择样本来帮助减少指数搜索空间。因此,否则将是NP难题,通过参与学习阶段的人员的输入和协助,极大地降低了复杂性。
更新日期:2019-11-01
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