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AREM: A Novel Associative Regression Model Based on EM Algorithm

机译：AREM：基于EM算法的新型联想回归模型

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摘要

In recent years, there have been increasing efforts in applying association rule mining to build Associative Classification (AC) models. However, the similar area that applies association rule mining to build Associative Regression (AR) models has not been well explored. In this work, we fill this gap by presenting a novel regression model based on association rules called AREM. AREM starts with finding a set of regression rules by applying the instance based pruning strategy, in which the best rules for each instance are discovered and combined. Then a probabilistic model is trained by applying the EM algorithm, in which the right hand side of the rules and their importance weights are updated. The extensive experimental evaluation shows that our model can perform better than both the previously proposed AR model and some of the state of the art regression models, including Boosted Regression Trees, SVR, CART and Cubist, with the Mean Squared Error (MSE) being used as the performance metric.
机译：近年来，在应用关联规则挖掘以建立关联分类（AC）模型方面，人们进行了越来越多的努力。但是，尚未很好地探索将关联规则挖掘应用于构建关联回归（AR）模型的类似领域。在这项工作中，我们通过提出一个基于称为AREM的关联规则的新型回归模型来填补这一空白。 AREM首先通过应用基于实例的修剪策略找到一组回归规则，在该策略中，发现并组合了每个实例的最佳规则。然后，通过应用EM算法训练概率模型，其中规则的右手边及其重要性权重被更新。广泛的实验评估表明，我们的模型可以比以前提出的AR模型和一些最新的回归模型（包括Boosted回归树，SVR，CART和Cubist）表现更好，并且使用均方误差（MSE）作为性能指标。

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