A Framework for Analytical Approaches to Combine Interpretable Models

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

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

Conference proceedings International

Title: Information Management and Big Data, 5th International Conference, SIMBig 2018, Lima, Peru, September 3-5, 2018, Proceedings.

Pages: 182-197

5th International Conference on Information Management and Big Data, SIMBig 2018

3 September 2018 through 5 September 2018

Indexing

Scopus - 2 Citations

Other information

Authenticus ID: P-00Q-5Z1

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

Abstract (EN): Analytic approaches to combine interpretable models, although presented in different contexts, can be generalized to highlight the components that can be specialized. We propose a framework that structures the combination process, formalizes the problems that can be solved in alternative ways and evaluates the combined models based on their predictive ability to replace the base ones, without loss of interpretability. The framework is illustrated with a case study using data from the University of Porto, Portugal, where experiments were carried out. The results show that grouping base models by scientific areas, ordering by the number of variables and intersecting their underlying rules creates conditions for the combined models to outperform them. © 2019, Springer Nature Switzerland AG.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 16