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

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Pedro Strecht (Author) REIT; João Mendes Moreira (Author) FEUP; Carlos Soares (Author) FEUP

Journal

Title: Expert Systems

Vol. 38

Pages: 1-40

ISSN: 0266-4720

Publisher: Wiley-Blackwell

Indexing

ISI Web of Knowledge - 3 Citations

Scopus - 2 Citations

Other information

Authenticus ID: P-00T-WVY

DOI: 10.1111/exsy.12702

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 40