Hybrid Algorithm Based on Simulated Annealing and Bacterial Foraging Optimization for Mining Imbalanced Data

Lee, Chou‐Yuan; Lee, Zne-Jung; Huang, Jian-Qiong; Ye, Fu-Lan; Yao, Jie; Ning, Zhang; Meen, Teen-­Hang

doi:10.18494/sam.2021.3167

Cited by 2 publications

(2 citation statements)

References 17 publications

(18 reference statements)

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“…These shortcomings necessitate further research efforts to enhance the performance of BFOA and address these deficiencies. The improvement of BFOA in the existing literature mainly focuses on the chemotaxis of bacteria, as shown in [61][62][63]. Chemotaxis, the core operation of the BFOA, relies heavily on the "chemotaxis step length" parameter C(i) for effective exploration.…”

Section: Proposed Improved Bacterial Foraging Optimization Algorithmmentioning

confidence: 99%

A Hybrid Model Based on LSSVM and the Improved BFOA for Sustainability of Daily Electricity Load Forecasting in Malaysia

Zaini,

Sulaima,

Razak

et al. 2024

Preprint

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: Leveraging the sustainability of the power system market, researchers have developed various ML models for forecasting electricity demand. The LSSVM is well suited to handle complex non-linear power load series. However, the less optimal regularization parameter and the Gaussian kernel function in the LSSVM model have contributed to flawed forecasting accuracy and random generalization ability. Thus, these parameters of LSSVM need to be chosen appropriately using intelligent optimization algorithms. This study proposes a hybrid model based on the LSSVM optimized by the IBFOA for forecasting the daily electricity load in Peninsular Malaysia. The IBFOA for parameter optimization of LSSVM is introduced. The IBFOA based on the sine cosine equation is proposed to adjust the constant step size in the BFOA, which creates an imbalance between the exploration and exploitation during optimization. Finally, the load forecasting model based on LSSVM-IBFOA is constructed using MAPE as the objective function. Comparative analysis demonstrates the model, achieving the highest R2 (0.9880) and significantly reducing error metrics (MAPE, MAE, RMSE, MSE, NRMSE) compared to the baseline LSSVM (average reduction of 27.72% to 47.72%). Additionally, IBFOA exhibits faster convergence and higher accuracy compared to BFOA, highlighting the accuracy of LSSVM-IBFOA for short-term load forecasting.

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Section: Proposed Improved Bacterial Foraging Optimization Algorithmmentioning

confidence: 99%

A Hybrid Model Based on LSSVM and the Improved BFOA for Sustainability of Daily Electricity Load Forecasting in Malaysia

Zaini,

Sulaima,

Razak

et al. 2024

Preprint

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“…Hybrid Algorithm Based on Simulated Annealing and Bacterial Foraging Optimization for Mining Imbalanced Data [45] BFO with Simulation Annealing…”

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confidence: 99%

A Survey Study on Proposed Solutions for Imbalanced Big Data

Razoqi,

Al-Talib

2024

Iraqi Journal of Science

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Learning from imbalanced data has been a focus of studies for more than two decades of continuous development. Training data is considered imbalanced when the size of the positive (minority) class is neglected because of the large size of the negative (majority) class, in addition to the problem of deviating distributions of binary tasks. The appearance of big data brings new problems and challenges to the imbalance problem. Big Data announces the challenges with 5V: volume, velocity, veracity, value, and variety. This study relied on dividing the solution to the problem of data imbalance into three levels: data level, algorithm level, and hybrid approaches. First, the standard solutions for this problem that were proposed were mentioned, and in addition, the most important metrics adopted for measuring the classification efficiency of imbalanced data were identified. In this survey study, 27 studies were reviewed during the period 2015–2022, distributed according to the levels of treatment of the imbalance problem. They also reviewed the performance metrics that were used in these studies and the sources of the datasets to which these solutions were applied. The study makes it easier for researchers and scholars to see the solutions to addressing the problem of data imbalance and the hybrid approaches recently used for that, and to take advantage of them in improving the classification process.

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Hybrid Algorithm Based on Simulated Annealing and Bacterial Foraging Optimization for Mining Imbalanced Data

Cited by 2 publications

References 17 publications

A Hybrid Model Based on LSSVM and the Improved BFOA for Sustainability of Daily Electricity Load Forecasting in Malaysia

A Hybrid Model Based on LSSVM and the Improved BFOA for Sustainability of Daily Electricity Load Forecasting in Malaysia

A Survey Study on Proposed Solutions for Imbalanced Big Data

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