Probabilistic Law Discovery (PLD) is a logic based Machine Learning method, which implements a variant of probabilistic rule learning. In several aspects, PLD is close to Decision Tree/Random Forest methods, but it differs significantly in how relevant rules are defined. The learning procedure of PLD solves the optimization problem related to the search for rules (called probabilistic laws), which have a minimal length and relatively high probability. At inference, ensembles of these rules are used for prediction. Probabilistic laws are human-readable and PLD based models are transparent and inherently interpretable. Applications of PLD include classification/clusterization/regression tasks, as well as time series analysis/anomaly detection and adaptive (robotic) control. In this paper, we outline the main principles of PLD, highlight its benefits and limitations and provide some application guidelines.
翻译:概率法律发现法(PLD)是一种基于逻辑的机理学习方法,它执行概率规则学习的变体,在若干方面,PLD接近决定树/兰多姆森林方法,但在如何界定相关规则方面差异很大。PLD的学习程序解决了与寻找规则(所谓的概率法律)有关的优化问题,这些规则的长度最小,概率较高。推断,这些规则的组合用于预测。概率法律是人类可读的,基于PLD的模型是透明和内在可解释的。PLD的应用包括分类/集群/递增任务,以及时间序列分析/异常检测和适应(roblogy)控制。在本文件中,我们概述了PLD的主要原则,突出其优点和局限性,并提供一些应用指南。