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首页> 外文会议>2011 IEEE International Conference on Systems, Man, and Cybernetics >Locally induced predictive models
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Locally induced predictive models

机译：局部诱导的预测模型

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摘要

Most predictive modeling techniques utilize all available data to build global models. This is despite the wellknown fact that for many problems, the targeted relationship varies greatly over the input space, thus suggesting that localized models may improve predictive performance. In this paper, we suggest and evaluate a technique inducing one predictive model for each test instance, using only neighboring instances. In the experimentation, several different variations of the suggested algorithm producing localized decision trees and neural network models are evaluated on 30 UCI data sets. The main result is that the suggested approach generally yields better predictive performance than global models built using all available training data. As a matter of fact, all techniques producing J48 trees obtained significantly higher accuracy and AUC, compared to the global J48 model. For RBF network models, with their inherent ability to use localized information, the suggested approach was only successful with regard to accuracy, while global RBF models had a better ranking ability, as seen by their generally higher AUCs.
机译：大多数预测建模技术都利用所有可用数据来构建全局模型。尽管存在众所周知的事实，即对于许多问题，目标关系在输入空间上变化很大，因此表明局部模型可以提高预测性能。在本文中，我们建议并评估一种仅使用相邻实例为每个测试实例引入一个预测模型的技术。在实验中，在30个UCI数据集上评估了建议算法产生局部决策树和神经网络模型的几种不同变体。主要结果是，与使用所有可用训练数据构建的全局模型相比，建议的方法通常可产生更好的预测性能。实际上，与全局J48模型相比，所有产生J48树的技术都获得了更高的准确性和AUC。对于RBF网络模型，由于其固有的使用局部信息的能力，建议的方法仅在准确性方面是成功的，而全局RBF模型具有更好的排名能力，这通常由其较高的AUC所见。

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