1 research outputs found
Modeling Sustainability Reporting with Ternary Attractor Neural Networks
International Conference on Mining Intelligence and Knowledge Exploration. Cluj-Napoca, Romania, December 20–22, 2018This work models the Corporate Sustainability General Reporting
Initiative (GRI) using a ternary attractor network. A dataset of
years evolution of the GRI reports for a world-wide set of companies was
compiled from a recent work and adapted to match the pattern coding for
a ternary attractor network. We compare the performance of the network
with a classical binary attractor network. Two types of criteria were used
for encoding the ternary network, i.e., a simple and weighted threshold,
and the performance retrieval was better for the latter, highlighting the
importance of the real patterns’ transformation to the three-state coding.
The network exceeds the retrieval performance of the binary network for
the chosen correlated patterns (GRI). Finally, the ternary network was
proved to be robust to retrieve the GRI patterns with initial noise.This work has been supported by Spanish grants MINECO
(http://www.mineco.gob.es/) TIN2014-54580-R, TIN2017-84452-R, and by UAMSantander CEAL-AL/2017-08, and UDLA-SIS.MG.17.02