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Ensemble of Adaptations for Classification: Learning Adaptation Rules for Categorical Features

机译：分类适应集：学习分类特征适应规则

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摘要

Acquiring knowledge for case adaptation is a classic challenge for case-based reasoning (CBR). To provide CBR systems with adaptation knowledge, machine learning methods have been developed for automatically generating adaptation rules. An influential approach uses the case difference heuristic (CDH) to generate rules by comparing pairs of cases in the case base. The CDH method has been studied for case-based prediction of numeric values (regression) from inputs with primarily numeric features, and has proven effective in that context. However, previous work has not attempted to apply the CDH method to classification tasks, to generate rules for adapting categorical solutions. This paper introduces an approach to applying the CDH to cases with categorical features and target values, based on the generalized case value difference heuristic (GCVDH). It also proposes a classification method using ensembles of GCVDH-generated rules, ensemble of adaptations for classification (EAC), an extension to our previous work on ensembles of adaptations for regression (EAR). It reports on an evaluation comparing the accuracy of EAC to three baseline methods on four standard domains, as well as comparing EAC to an ablation relying on single adaptation rules, and assesses the effect of training/test size on accuracy. Results are encouraging for the effectiveness of the GCVDH approach and for the value of applying ensembles of learned adaptation rules for classification.
机译：获取基于案例的适应性知识是基于案例的推理（CBR）的经典挑战。为了向CBR系统提供适应性知识，已经开发了机器学习方法来自动生成适应性规则。一种有影响力的方法是使用案例差异启发式（CDH）通过比较案例库中的成对案例来生成规则。已经对CDH方法进行了研究，以便从具有主要数字特征的输入中进行基于案例的数字值预测（回归），并已证明在这种情况下有效。但是，以前的工作尚未尝试将CDH方法应用于分类任务，以生成适用于分类解决方案的规则。本文基于广义案例价值差异启发式算法（GCVDH），介绍了一种将CDH应用于具有分类特征和目标值的案例的方法。它还提出了一种使用GCVDH生成的规则集合，分类适应集合（EAC）的集合的分类方法，这是对我们之前关于回归适应集合（EAR）的工作的扩展。它报告了一项评估，该评估将EAC与四个标准域上的三种基线方法的准确性进行了比较，以及将EAC与依赖于单一适应规则的消融进行了比较，并评估了训练/测试量对准确性的影响。对于GCVDH方法的有效性以及将学习到的适应规则集合应用于分类的价值，结果令人鼓舞。

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