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Information-based classification by aggregating emerging patterns

机译:通过聚合新兴模式基于信息的分类

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摘要

Emerging patterns (EPs) are knowledge patterns capturing contrasts between data classes. In this paper, we propose an information-based approach for classification by aggregating emerging patterns. The constraint-based EP mining algorithm enables the system to learn from large-volume and high-dimensional data; the new approach for selecting representative EPs and efficient algorithm for finding the EPs renders the system high predictive accuracy and short classification time. Experiments on many benchmark datasets show that the resulting classifiers have good overall predictive accuracy, and are often also superior to other state-of-the-art classification systems such as C4.5, CEA and LB.
机译:新兴模式(EPS)是知识模式捕获数据类之间的对比。在本文中,我们提出了一种通过聚合出现的模式来分类的基于信息的方法。基于约束的EP挖掘算法使系统能够从大容量和高维数据中学习;选择代表性EPS和高效算法的新方法使系统具有高预测精度和短分量时间。在许多基准数据集上的实验表明,由此产生的分类器具有良好的整体预测精度,并且通常也优于其他最先进的分类系统,例如C4.5,CEA和LB。

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