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Bucket Learning: Improving model quality through enhancing local patterns

机译：桶学习：通过增强局部模式来提高模型质量

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摘要

It is always desirable to improve the quality of a global classification model in the light of the existing models. In this work, the Bucket Learning methodology is first proposed to improve the model quality by enhancing its local patterns. We formally define the concept of a board as a tri-tuple (D.M.R), which unifies the data view, model view and evaluation view of a data mining task. The Bucket Learning frame work includes the modules of Boards Generation, Short Boards Discovery, and Short Boards Replacement. A prototypical system is developed to verify the proposed methodology. The experimental results on eight representative data sets from the UCI data repository show that Bucket Learning performs better than tra ditional classification methods such as J48, AdaBoost, Bagging and LogitBoost. We also demonstrate that the Bucket Learning framework can combine all kinds of data classification models and that the combined model outperforms each individual one.
机译：总是期望根据现有模型来改善全局分类模型的质量。在这项工作中，首先提出了Bucket Learning方法，以通过增强其局部模式来提高模型质量。我们正式将板的概念定义为三元组（D.M.R），它将数据挖掘任务的数据视图，模型视图和评估视图统一起来。桶学习框架包括板生成，短板发现和短板更换的模块。开发了一个原型系统来验证所提出的方法。对来自UCI数据仓库的八个代表性数据集的实验结果表明，Bucket Learning的性能优于传统分类方法，例如J48，AdaBoost，Bagging和LogitBoost。我们还证明了Bucket Learning框架可以组合各种数据分类模型，并且组合模型的性能优于每个模型。

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