1 code implementation • 13 Apr 2023 • Sonia Bullah, Terence L. Van Zyl
The results of this study show that, despite taking significantly longer to run to completion, the learnheuristic algorithms outperform the baseline algorithms in terms of hypervolume and rate of convergence.
no code implementations • 20 Mar 2023 • Terence L. Van Zyl
However, a unified taxonomy for model fusion and an empirical comparison of these hybrid and feature-based stacking ensemble approaches is still missing.
no code implementations • 5 Nov 2022 • Gift Khangamwa, Terence L. Van Zyl, Clint J. van Alten
Our study's contribution is a methodology for tackling missing data problems using a combination of synthetic dataset generation, missing data imputation and deep learning methods to resolve missing data challenges.
no code implementations • 31 Oct 2022 • Mohamed Z. Variawa, Terence L. Van Zyl, Matthew Woolway
The results also highlight the need to tune the hyper-parameters used by the surrogate-assisted framework, as the surrogate, in some instances, shows some deterioration over the baseline algorithm.
no code implementations • 13 Oct 2022 • Mufhumudzi Muthivhi, Terence L. Van Zyl, Hairong Wang
Context-aware recommendation systems improve upon classical recommender systems by including, in the modelling, a user's behaviour.
1 code implementation • 13 Sep 2022 • Terence L. Van Zyl, Matthew Woolway, Andrew Paskaramoorthy
Portfolio management is a multi-period multi-objective optimisation problem subject to a wide range of constraints.
no code implementations • 21 Jun 2022 • Nimesh Bhana, Terence L. Van Zyl
Language Models such as BERT have grown in popularity due to their ability to be pre-trained and perform robustly on a wide range of Natural Language Processing tasks.
no code implementations • 28 Mar 2022 • Liezl Stander, Matthew Woolway, Terence L. Van Zyl
We further find that combining a Genetic Algorithm framework with Machine Learning Surrogate models as a substitute for long-running simulation models yields significant computational efficiency improvements, 1. 7 - 1. 84 times speedup for the increased complexity examples and a 2. 7 times speedup for the Pressure Swing Adsorption system.
no code implementations • 10 Mar 2022 • Mufhumudzi Muthivhi, Terence L. Van Zyl
This paper aims to unpack and develop an enhanced understanding of the sentiment aware portfolio selection problem.
1 code implementation • 16 Dec 2021 • Thabang Mathonsi, Terence L. Van Zyl
Difficulties with applying hybrid forecast methods to multivariate data include ($i$) the high computational cost involved in hyperparameter tuning for models that are not parsimonious, ($ii$) challenges associated with auto-correlation inherent in the data, as well as ($iii$) complex dependency (cross-correlation) between the covariates that may be hard to capture.
Multivariate Time Series Forecasting Prediction Intervals +1
no code implementations • 7 Oct 2021 • Thabang Mathonsi, Terence L. Van Zyl
We benchmark our approach against the oft-preferred well-established statistical models.
no code implementations • 4 Oct 2021 • Jiahao Huo, Terence L. Van Zyl
We show that one does not require stored images (exemplars) for incremental learning with similarity learning.
no code implementations • 19 Aug 2021 • Pieter Cawood, Terence L. Van Zyl
We investigate ensembling techniques in forecasting and examine their potential for use in nonseasonal time-series similar to those in the early days of the COVID-19 pandemic.
no code implementations • 25 Mar 2021 • Daniel Yazbek, Jonathan Sandile Sibindi, Terence L. Van Zyl
Six models were developed and compared, a LightGBM, a XGBoost, a LightGBM (Contrastive Loss), LightGBM (Triplet Loss), a XGBoost (Contrastive Loss), XGBoost (Triplet Loss).