To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Estimators
To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Estimators
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DOI：
10.1109/tkde.2007.190650
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发表时间：
2007-12
影响因子：
8.9
通讯作者：
Ying Yang;Geoffrey I. Webb;J. Cerquides;K. Korb;Janice R. Boughton;K. Ting
中科院分区：
文献类型：
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作者：
Ying Yang;Geoffrey I. Webb;J. Cerquides;K. Korb;Janice R. Boughton;K. Ting
我们对线性组合超父一相关估计器（SPODEs）进行了大规模的比较研究，SPODEs是一类流行的语义贝叶斯分类器。总共采用了16种模型选择和加权方案，58个基准数据集，以及各种统计检验。本文的主要贡献有三个方面。首先，它正式地介绍了每个方案的定义、基本原理和时间复杂度，因此可以作为对集成学习感兴趣的研究人员的全面参考。其次，对各方案的分类误差性能进行了偏方差分析。第三，确定了满足实践中各种需求的有效方案。这导致了准确和快速的分类算法，对现实世界的应用产生了直接和重大的影响。我们研究的另一个重要特征是使用各种统计测试来评估跨多个数据集的多种学习方法。
We conduct a large-scale comparative study on linearly combining superparent-one-dependence estimators (SPODEs), a popular family of seminaive Bayesian classifiers. Altogether, 16 model selection and weighing schemes, 58 benchmark data sets, and various statistical tests are employed. This paper's main contributions are threefold. First, it formally presents each scheme's definition, rationale, and time complexity and hence can serve as a comprehensive reference for researchers interested in ensemble learning. Second, it offers bias-variance analysis for each scheme's classification error performance. Third, it identifies effective schemes that meet various needs in practice. This leads to accurate and fast classification algorithms which have an immediate and significant impact on real-world applications. Another important feature of our study is using a variety of statistical tests to evaluate multiple learning methods across multiple data sets.