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A Multi-Tier Stacked Ensemble Algorithm for Improving Classification Accuracy
Computing in Science & Engineering ( IF 2.1 ) Pub Date : 2020-07-01 , DOI: 10.1109/mcse.2018.2873940
Ramalingam Pari 1 , Maheshwari Sandhya 1 , Sharmila Sankar 1
Affiliation  

For real-world problems, ensemble learning performs better than the individual classifiers. This is true for datasets that have many instances closer to the decision boundary. Using a meta-learner to learn from the predictions of the base classifiers generalizes better. Hence, stacked ensemble (SE) is preferred over other ensemble methods. We extend the SE and propose a multitier stacked ensemble (MTSE) algorithm with three tiers, namely, a base tier, an ensemble tier, and a generalization tier. The base tier uses the traditional classifiers to predict the labels. Tenfold cross-validation is used to validate the models in the base tiers. The cross-validated predictions are combined using combination schemes in the next tier. The predictions from the ensemble tier are generalized using meta-learning to give the final prediction. When tested with 36 datasets, the MTSE gives superior performance over the SE. It achieves high accuracy and does not suffer from over-fitting/under-fitting.

中文翻译:

一种提高分类精度的多层堆叠集成算法

对于现实世界的问题,集成学习比单个分类器表现得更好。对于具有许多靠近决策边界的实例的数据集来说,这是正确的。使用元学习器从基本分类器的预测中学习可以更好地泛化。因此,堆叠集成(SE)优于其他集成方法。我们扩展了 SE 并提出了一种具有三层的多层堆叠集成 (MTSE) 算法,即基础层、集成层和泛化层。基础层使用传统分类器来预测标签。十倍交叉验证用于验证基础层中的模型。使用下一层中的组合方案组合交叉验证的预测。来自集成层的预测使用元学习进行泛化以给出最终预测。当使用 36 个数据集进行测试时,MTSE 的性能优于 SE。它实现了高精度并且不会受到过拟合/欠拟合的影响。
更新日期:2020-07-01
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