EDGE: Evolutionary Directed Graph Ensembles

• Type: Article in International Scientific Journal
• Date: 2019

Authors

Xavier Fontes (Author) FEUP; Daniel Castro Silva (Author) FEUP

Journal

The Journal is awaiting validation by the Administrative Services.

Title: Int. J. Hybrid Intell. Syst.

Vol. 15

Pages: 243-256

ISSN: 1448-5869

Indexing

Scopus

Other information

Authenticus ID: P-00T-MZX

DOI: 10.3233/his-190273

Abstract (EN): This paper introduces Evolutionary Directed Graph Ensembles (EDGE). EDGE combines ideas from social dynamics and trust in human beings with graph theory. We use pre-trained prediction models as nodes in a directed acyclic graph where the connections between nodes have associated weight matrices to simulate the trust each node has in its predecessors. EDGE uses a genetic algorithm approach to evolve a population of these directed acyclic graphs, in an ensemble-type hybridization process. The pre-trained models are stored in a pool of models named Reservoir. The Reservoir can be populated with models from different families, such as Decision Trees, Ensemble Methods or Neural Networks. To test EDGE, four datasets were used: a dataset of a parking lot occupancy taken from a university student parking lot; a dataset about appliances energy use in a low energy building; a dataset of Anuran calls; and the MNIST dataset. Results show that we can achieve good accuracy measures of around 98% for the MNIST dataset, 99% for the Anuran data, 86% on the Appliances Energy and about 88% on the parking lot dataset. EDGE proves to be robust against bad performing nodes, presenting average accuracy results of up to 49% above the worst performing node in the ensemble. It also never shows results below the best performing node, and in some cases even improves the results with respect to the best node by up to 4%. © 2019 - IOS Press and the authors. All rights reserved.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13