Robust Bayesian linear classifier ensembles
Robust Bayesian linear classifier ensembles
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DOI：
10.1007/11564096_12
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发表时间：
2005-01-01
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通讯作者：
de Màntaras, RL
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文献类型：
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作者：
Cerquides, J;de Màntaras, RL
包围式分类器联合收割机将多个分类器的分类结果进行组合。简单的集成方法，如统一平均一组模型通常提供了一个选择单一的最佳模型的改进。通常概率分类器限制可以学习的可能模型的集合，以降低计算复杂性成本。在这些有限的空间中，可能会做出不正确的建模假设，均匀平均有时甚至比贝叶斯模型平均更好。模型集上的线性混合提供了一个空间，该空间包括作为特定情况的均匀平均。我们开发了两个算法学习线性混合的最大后验权重，基于期望最大化和约束优化。我们提供了一个非平凡的例子，这两个算法的效用，将它们应用于一个依赖估计。我们发展了一个相依估计量的共轭分布，并实证表明，均匀平均明显优于上级贝叶斯模型平均这类模型，之后，我们实证表明，最大后验线性混合权重提高精度显着超过均匀聚集。
Ensemble classifiers combine the classification results of several classifiers. Simple ensemble methods such as uniform averaging over a set of models usually provide an improvement over selecting the single best model. Usually probabilistic classifiers restrict the set of possible models that can be learnt in order to lower computational complexity costs. In these restricted spaces, where incorrect modeling assumptions are possibly made, uniform averaging sometimes performs even better than bayesian model averaging. Linear mixtures over sets of models provide an space that includes uniform averaging as a particular case. We develop two algorithms for learning maximum a posteriori weights for linear mixtures, based on expectation maximization and on constrained optimizition. We provide a nontrivial example of the utility of these two algorithms by applying them for one dependence estimators. We develop the conjugate distribution for one dependence estimators and empirically show that uniform averaging is clearly superior to Bayesian model averaging for this family of models, After that we empirically show that the maximum a posteriori linear mixture weights improve accuracy significantly over uniform aggregation.