An improved approach to Arabic news classification based on hyperparameter tuning of machine learning algorithms

Jamaleddyn, Imad; Ayachi, Rachid El; Bahaj, Mohamed

doi:10.1016/j.jer.2023.100061

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Machine learning algorithms were applied to the data through supervised learning [33,34]. The adjustment of hyperparameters was evaluated to optimize the folds, iterations, and estimator of the model with different learning methods such as default mode, tuned mode, voting mode, and stacking mode [35][36][37][38].…”

Section: Forwarder Productivity Modelingmentioning

confidence: 99%

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Tactical Forwarder Planning: A Data-Driven Approach for Timber Forwarding

Munis,

Almeida,

Camargo

et al. 2023

Forests

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Tactical planning in timber harvesting involves aspects related to forest macro-planning and, particularly, the allocation of resources and sequencing of activities, all of which affect the allocation of timber in forest yards and roads and the productivity of forest machines. Data-driven approaches encourage the use of information obtained from data to enhance decision-making efficiency and support the development of short-term strategies. Therefore, our investigation was intended to determine whether a data-driven approach can generate sufficient input for modeling forwarder productivity in timber forwarding in Pinus and Eucalyptus planted forests, to support tactical planning. We utilized 3812 instances of raw data that were generated over a 36-month period. The data were collected from 23 loggers who operated in Pinus and Eucalyptus planted forests. We applied 22 regression algorithms that applied a supervised learning method from an experimental machine learning approach to the data instances. We evaluated the fitted models using three performance metrics. Out of the tested algorithms, the default mode of light gradient boosting produced a root mean squared error of 14.80 m3 h−1, a mean absolute error of 2.70, and a coefficient of determination of 0.77. Therefore, data-driven methods adequately support forwarder productivity modeling in timber forwarding in planted forests and help forest managers with tactical planning.

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Section: Forwarder Productivity Modelingmentioning

confidence: 99%

“…To develop models capable of predicting forwarder productivity and optimizing their output, hyperparameter tuning is necessary. Hyperparameter tuning is the automatic optimization of a machine learning model's parameters [36].…”

Section: Forwarder Productivity Modelingmentioning

confidence: 99%

Tactical Forwarder Planning: A Data-Driven Approach for Timber Forwarding

Munis,

Almeida,

Camargo

et al. 2023

Forests

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Random Search Hyperparameter Optimization for BPNN to Forecasting Cattle Population

Khotimah,

Agustina,

Puspitarini

et al. 2024

E3S Web Conf.

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Backpropagation Neural Network (BPNN) is a suitable method for predicting the future. It has weaknesses, namely poor convergence speed and instability, requiring parameter tuning to overcome speed problems, and having a high bias. This research uses the Random Search hyperparameter technique to optimize BPNN to automatically select the number of hidden layers, learning rate, and momentum. The added accuracy of momentum will speed up the training process, produce predictions with better accuracy, and determine the best architectural model from a series of faster training processes with low bias. This research will predict the local Indonesian cattle population, which is widely developed by people in the eastern part, especially Madura, in 4 types of cattle: sono cattle, karapan cattle, mixed cattle, and breeder cattle. The results of BPNN hyperparameter measurements with the best model show that hyperparameter optimization did not experience overfitting and experienced an increase in accuracy of 2.5% compared to the Neural Network model without hyperparameter optimization. Based on the test results, the BPNN algorithm parameters with a data ratio of 70:30, the best architecture for backpropagation momentum is 6-6-1, with a learning rate of 0.002, momentum 0.3, which has an MSE during testing of 0.1176 on Karapan type Madurese cattle. Tests based on computing time measurements show that the BPNN hyperparameter algorithm stops at 490 iterations compared to regular BPNN. The research results show that the hidden layers, learning rate, and momentum if optimized simultaneously, have a significant influence in preventing overfitting, increasing accuracy, and having better execution times than without optimization.

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