Mixing independent classifiers
Mixing independent classifiers
复制标题
混合独立分类器
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
Alwyn Barry
中科院分区：
文献类型：
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作者：
Jan Drugowitsch;Alwyn Barry
在这项研究中，我们处理混合问题，它涉及到结合独立训练的局部模型的预测来形成全局预测。我们从学习分类器系统的角度来处理它，其中一组分类器提供局部模型。首先，我们形式化混合问题，并提供分析和启发式方法来解决它。分析方法被证明不能很好地随局部模型的数量而扩展，但仍然在一组函数逼近任务中与启发式模型相比较。这些实验表明，我们可以设计超过当前最先进的学习分类器系统XCS的性能的启发式算法，并且与分析解决方案相比具有竞争力。此外，我们还给出了启发式混合方法的预测误差的上界。
In this study we deal with the mixing problem, which concerns combining the prediction of independently trained local models to form a global prediction. We deal with it from the perspective of Learning Classifier Systems where a set of classifiers provide the local models. Firstly, we formalise the mixing problem and provide both analytical and heuristic approaches to solving it. The analytical approaches are shown to not scale well with the number of local models, but are nevertheless compared to heuristic models in a set of function approximation tasks. These experiments show that we can design heuristics that exceed the performance of the current state-of-the-art Learning Classifier System XCS, and are competitive when compared to analytical solutions. Additionally, we provide an upper bound on the prediction errors for the heuristic mixing approaches.