A Framework for Analytical Approaches to Combine Interpretable Models

Strecht, Pedro; Mendes-Moreira, João; Soares, Carlos

doi:10.1007/978-3-030-11680-4_19

Cited by 1 publication

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“…One approach to this problem is to generate a dataset representative of the decision table, which was explored in our previous works (Strecht et al, 2014(Strecht et al, , 2019 and mentioned by Andrzejak et al (2013) All rules datasets are then assembled together into one dataset D [Equation ( 36)] from which a model Ω is trained [Equation ( 37)].…”

Section: Other Combination Scenariosmentioning

confidence: 99%

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Inmplode: A framework to interpret multiple related rule‐based models

2021

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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.

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Section: Other Combination Scenariosmentioning

confidence: 99%

“…We have previously published a methodology to merge models intertwined with a case study (Strecht et al, 2014). Subsequently, we have presented it as a framework (Strecht et al, 2019) separating it from the case study. These works are extended in this paper by improving the methodology while expanding both the framework and its experimental evaluation.…”

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confidence: 99%