Using good and bad diversity measures in the design of ensemble systems: A genetic algorithm approach

Neto, Antonino A. Feitosa; Canuto, Anne M. P.; Ludermir, Teresa B.

doi:10.1109/cec.2013.6557649

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…When designing a predictive model for time-series application based on ensemble method, the size affect performance of model. As the number of base models increases, the accuracy improves but will lead to increasing in the computational time and storage space [78]. Determining the optimal number of base models is a research challenge.…”

Section: Discussion Of Related Workmentioning

confidence: 99%

On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges

Surakhi¹,

Serhan²,

Salah³

2020

Adv. sci. technol. eng. syst. j.

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Time-series is a sequence of observations that are taken sequentially over time. Modelling a system that generates a future value from past observations is considered as time-series forecasting system. Recurrent neural network is a machine learning method that is widely used in the prediction of future values. Due to variant improvements on recurrent neural networks, choosing of the best model for better prediction generation is dependent on problem domain and model design characteristics. Ensemble forecasting is more accurate than single model due to the combination of more than one model for forecasting. Designing an ensemble model of recurrent neural network for time-series forecasting applications would enhance prediction accuracy and improve performance. This paper highlights some of the challenges that are faced by the design of the ensemble model of different recurrent neural network versions, and surveys some of the most relevant works in order to give a direction of how to conduct ensemble learning research in the future. Based on the reviewed literature, we propose a framework for time-series forecasting based on the using of ensemble technique.

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Section: Discussion Of Related Workmentioning

confidence: 99%