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From global to local and viceversa: uses of associative rule learning for classification in imprecise environments

机译：从全局到本地，反之亦然：在不精确的环境中使用关联规则学习进行分类

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摘要

We propose two models for improving the performance of rule-based classification under unbalanced and highly imprecise domains. Both models are probabilistic frameworks aimed to boost the performance of basic rule-based classifiers. The first model implements a global-to-local scheme, where the response of a global rule-based classifier is refined by performing a probabilistic analysis of the coverage of its rules. In particular, the coverage of the individual rules is used to learn local probabilistic models, which ultimately refine the predictions from the corresponding rules of the global classifier. The second model implements a dual local-to-global strategy, in which single classification rules are combined within an exponential probabilistic model in order to boost the overall performance as a side effect of mutual influence. Several variants of the basic ideas are studied, and their performances are thoroughly evaluated and compared with state-of-the-art algorithms on standard benchmark datasets.
机译：我们提出了两种模型来提高不平衡和高度不精确域下基于规则的分类的性能。这两个模型都是概率框架，旨在提高基于规则的基本分类器的性能。第一个模型实现了全局到局部的方案，其中基于全局规则的分类器的响应通过对其规则的覆盖范围进行概率分析而得到改进。特别地，各个规则的覆盖范围用于学习局部概率模型，该模型最终根据全局分类器的相应规则来完善预测。第二个模型实现了从本地到全局的双重策略，其中将单个分类规则组合在指数概率模型中，以提高总体性能，这是相互影响的副作用。研究了基本概念的几种变体，并对它们的性能进行了全面评估，并与标准基准数据集上的最新算法进行了比较。

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