There is a growing trend to split problems into separate subproblems and develop separate models for each (e.g., different churn models for separate customer segments; different failure prediction models for separate university courses, etc.). While it may lead to better predictive models, the use of multiple models makes interpretability more challenging. In this paper, we address the problem of synthesizing the knowledge contained in a set of models without a significant loss of prediction performance. We focus on decision tree models because their interpretability makes them suitable for problems involving knowledge extraction. We detail the process, identifying alternative methods to address the different phases involved. An extensive set of experiments is carried out on the problem of predicting the failure of students in courses at the University of Porto. We assess the effect of using different methods for the operations of the methodology, both in terms of the knowledge extracted as well as the accuracy of the combined models.

中文翻译：

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Inmplode：一个解释多个相关的基于规则的模型的框架

将问题分解为单独的子问题并为每个子问题开发单独模型的趋势越来越大（例如，针对不同的客户细分市场采用不同的流失模型；针对不同的大学课程采用不同的故障预测模型等）。虽然它可能会产生更好的预测模型，但使用多个模型会使可解释性更具挑战性。在本文中，我们解决了在不显着损失预测性能的情况下综合包含在一组模型中的知识的问题。我们专注于决策树模型，因为它们的可解释性使其适用于涉及知识提取的问题。我们详细介绍了该过程，确定了解决所涉及不同阶段的替代方法。波尔图大学针对预测学生课程失败的问题进行了大量实验。我们评估使用不同方法进行方法操作的效果，无论是在提取的知识还是组合模型的准确性方面。