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A hybrid classification method using error pattern modeling

机译：使用错误模式建模的混合分类方法

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摘要

This paper presents a new hybrid classification method using error pattern modeling to improve classification accuracy when the data type of a target variable is binary. The proposed method tries to increase prediction accuracy by combining two different supervised learning methods. That is, the algorithm extracts a subset of training cases that are predicted inconsistently by two methods, and the extracted data subset is used to learn when each method works better. The learned discrimination model is called error pattern models and is used to merge the prediction results of two different methods to generate final prediction. The proposed method has been tested using 13 real-world data sets. The analysis results show that the performance of the proposed method is superior to other hybrid methods and the single usage of existing classification methods such as artificial neural networks and decision tree induction. In particular when prediction inconsistency ratio of the two methods is high, the proposed hybrid method provides significant improvement of prediction accuracy.
机译：本文提出了一种新的使用错误模式建模的混合分类方法，以提高目标变量的数据类型为二进制时的分类精度。所提出的方法试图通过结合两种不同的监督学习方法来提高预测精度。也就是说，该算法提取了两种方法不一致预测的训练案例的子集，并且提取的数据子集用于学习每种方法何时更好地工作。学习到的判别模型称为错误模式模型，用于合并两种不同方法的预测结果以生成最终预测。所提出的方法已使用13个实际数据集进行了测试。分析结果表明，该方法的性能优于其他混合方法，并且仅使用现有分类方法（如人工神经网络和决策树归纳法）。特别是当两种方法的预测不一致率较高时，所提出的混合方法将大大提高预测精度。

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