Local Boosting of Decision Stumps for Regression and Classification Problems
Local Boosting of Decision Stumps for Regression and Classification Problems
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DOI：
10.4304/jcp.1.4.30-37
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发表时间：
2006-07-01
期刊：
影响因子：
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通讯作者：
Pintelas, P. E.
中科院分区：
文献类型：
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作者：
Kotsiantis, S. B.;Kanellopoulos, D.;Pintelas, P. E.
许多数据挖掘问题都涉及对异构数据集中特征之间关联的研究，其中不同的预测模型可能更适合不同的区域。我们提出了一种增强局部弱学习器的技术；我们允许权重是输入域上的函数，而不是为每个学习器附加恒定的权重（如标准增强方法）。为了找出这些函数，我们识别具有相似特征的局部区域，然后在每个区域上构建局部专家来描述数据特征与目标值之间的关联。我们使用决策树桩作为基础学习器，在标准分类和回归基准数据集上与其他众所周知的组合方法进行了比较，所提出的技术产生了最准确的结果。
Numerous data mining problems involve an investigation of associations between features in heterogeneous datasets, where different prediction models can be more suitable for different regions. We propose a technique of boosting localized weak learners; rather than having constant weights attached to each learner (as in standard boosting approaches), we allow weights to be functions over the input domain. In order to find out these functions, we recognize local regions having similar characteristics and then build local experts on each of these regions describing the association between the data characteristics and the target value. We performed a comparison with other well known combining methods on standard classification and regression benchmark datasets using decision stump as based learner, and the proposed technique produced the most accurate results.