SOAR：正反类分类的同时或与规则
SOAR: Simultaneous Or of And Rules for Classification of Positive & Negative Classes
摘要：
商业决策已经扩散，现在以平凡和重要的方式影响着我们的日常生活。机器学习从业者在电影推荐、医疗诊断和假释推荐等各种应用中使用了大量的预测模型算法，而没有深入研究驱动特定预测决策的原因。这些应用中的机器学习算法通常因其优异的性能而被选择，然而诸如随机森林和深度神经网络等流行的选择无法提供对预测模型的可解释的理解。近年来，基于规则的算法已被用来解决这个问题。Wang et al.（2017）提出了一种基于or-of-and（析取范式）的分类技术，该技术允许在二进制分类中对单个类进行分类规则挖掘;这种方法也被证明可以执行其他现代算法。在这项工作中，我们扩展了这个想法，同时为这两个类提供分类规则。也就是说，我们为正类和负类提供了一组不同的规则。在描述这种方法时，我们还提出了一种新颖且完整的分类分类法，可以清晰地捕获和量化现实世界中嘈杂的二元分类中固有的模糊性。我们表明，这种方法导致一个更细粒度的制定的似然模型和基于模拟退火的优化实现分类性能与可比技术的竞争。我们将我们的方法应用于合成以及现实世界的数据集，与其他相关的方法，证明我们的建议的实用性进行比较。
英文摘要：
Algorithmic decision making has proliferated and now impacts our daily lives in both mundane and consequential ways. Machine learning practitioners make use of a myriad of algorithms for predictive models in applications as diverse as movie recommendations, medical diagnoses, and parole recommendations without delving into the reasons driving specific predictive decisions. Machine learning algorithms in such applications are often chosen for their superior performance, however popular choices such as random forest and deep neural networks fail to provide an interpretable understanding of the predictive model. In recent years, rule-based algorithms have been used to address this issue. Wang et al. (2017) presented an or-of-and (disjunctive normal form) based classification technique that allows for classification rule mining of a single class in a binary classification; this method is also shown to perform comparably to other modern algorithms. In this work, we extend this idea to provide classification rules for both classes simultaneously. That is, we provide a distinct set of rules for both positive and negative classes. In describing this approach, we also present a novel and complete taxonomy of classifications that clearly capture and quantify the inherent ambiguity in noisy binary classifications in the real world. We show that this approach leads to a more granular formulation of the likelihood model and a simulated-annealing based optimization achieves classification performance competitive with comparable techniques. We apply our method to synthetic as well as real world data sets to compare with other related methods that demonstrate the utility of our proposal.
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