• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Pacific-Asia Conference on Knowledge Discovery and Data Mining >AREM: A Novel Associative Regression Model Based on EM Algorithm
【24h】

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

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

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，在应用关联规则采矿方面越来越努力建立联想分类（AC）模型。但是，应用关联规则挖掘建立关联回归（AR）模型的类似区域并未得到很好的探索。在这项工作中，我们通过呈现基于名为AREM的关联规则的新型回归模型来填补这种差距。 AREM通过应用基于实例的修剪策略来查找一组回归规则，其中发现并组合每个实例的最佳规则。然后通过应用EM算法训练概率模型，其中更新规则的右侧及其重要性权重。广泛的实验评估表明，我们的模型可以比以前提出的AR模型和一些最先进的回归模型的状态更好，包括升级的回归树，SVR，推车和立体师，使用平均平方误差（MSE）作为性能度量。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号