An intelligent feature selection approach based on moth flame optimization for medical diagnosis

Khurmaa, Ruba Abu; Aljarah, Ibrahim; Sharieh, Ahmad

doi:10.1007/s00521-020-05483-5

Cited by 47 publications

(31 citation statements)

References 45 publications

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“…A metaheuristic is a developed optimization algorithm with a high-level problem independent framework [ 38 , 39 , 40 ]. The best solution in the metaheuristic algorithm is found out of all possible solutions of an optimization.…”

Section: Methodsmentioning

confidence: 99%

A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network

Alhmoud

Khurma

Al-Zoubi

et al. 2021

Sensors

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Power system planning and expansion start with forecasting the anticipated future load requirement. Load forecasting is essential for the engineering perspective and a financial perspective. It effectively plays a vital role in the conventional monopolistic operation and electrical utility planning to enhance power system operation, security, stability, minimization of operation cost, and zero emissions. Two Well-developed cases are discussed here to quantify the benefits of additional models, observation, resolution, data type, and how data are necessary for the perception and evolution of the electrical load forecasting in Jordan. Actual load data for more than a year is obtained from the leading electricity company in Jordan. These cases are based on total daily demand and hourly daily demand. This work’s main aim is for easy and accurate computation of week ahead electrical system load forecasting based on Jordan’s current load measurements. The uncertainties in forecasting have the potential to waste money and resources. This research proposes an optimized multi-layered feed-forward neural network using the recent Grey Wolf Optimizer (GWO). The problem of power forecasting is formulated as a minimization problem. The experimental results are compared with popular optimization methods and show that the proposed method provides very competitive forecasting results.

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Section: Methodsmentioning

confidence: 99%

A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network

Alhmoud

Khurma

Al-Zoubi

et al. 2021

Sensors

Self Cite

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“…FS performs a dimensionality reduction by eliminating noisy symmetrical features and keeps the most informative features in a dataset. The noisy features include features that have a high correlation with other symmetrical features (redundant) and features that have a weak correlation with the target class (label of the instance) (irrelevant) [27]. The major advantage of applying FS is the construction of a reduced version of the dataset, which reduces the cost of the learning process in terms of time and hardware resources.…”

Section: Feature Selectionmentioning

confidence: 99%

An Evolutionary Fake News Detection Method for COVID-19 Pandemic Information

et al. 2021

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As the COVID-19 pandemic rapidly spreads across the world, regrettably, misinformation and fake news related to COVID-19 have also spread remarkably. Such misinformation has confused people. To be able to detect such COVID-19 misinformation, an effective detection method should be applied to obtain more accurate information. This will help people and researchers easily differentiate between true and fake news. The objective of this research was to introduce an enhanced evolutionary detection approach to obtain better results compared with the previous approaches. The proposed approach aimed to reduce the number of symmetrical features and obtain a high accuracy after implementing three wrapper feature selections for evolutionary classifications using particle swarm optimization (PSO), the genetic algorithm (GA), and the salp swarm algorithm (SSA). The experiments were conducted on one of the popular datasets called the Koirala dataset. Based on the obtained prediction results, the proposed model revealed an optimistic and superior predictability performance with a high accuracy (75.4%) and reduced the number of features to 303. In addition, by comparison with other state-of-the-art classifiers, our results showed that the proposed detection method with the genetic algorithm model outperformed other classifiers in the accuracy.

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“…However, MFO inherits the drawbacks of swarm intelligence algorithms, such as stagnation in local minima and premature convergence. To address these shortcomings, the improvement of the MFO algorithm has been proposed [28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Evolutionary Software Defect Prediction Method Using Island Moth Flame Optimization

et al. 2021

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Software defect prediction (SDP) is crucial in the early stages of defect-free software development before testing operations take place. Effective SDP can help test managers locate defects and defect-prone software modules. This facilitates the allocation of limited software quality assurance resources optimally and economically. Feature selection (FS) is a complicated problem with a polynomial time complexity. For a dataset with N features, the complete search space has 2N feature subsets, which means that the algorithm needs an exponential running time to traverse all these feature subsets. Swarm intelligence algorithms have shown impressive performance in mitigating the FS problem and reducing the running time. The moth flame optimization (MFO) algorithm is a well-known swarm intelligence algorithm that has been used widely and proven its capability in solving various optimization problems. An efficient binary variant of MFO (BMFO) is proposed in this paper by using the island BMFO (IsBMFO) model. IsBMFO divides the solutions in the population into a set of sub-populations named islands. Each island is treated independently using a variant of BMFO. To increase the diversification capability of the algorithm, a migration step is performed after a specific number of iterations to exchange the solutions between islands. Twenty-one public software datasets are used for evaluating the proposed method. The results of the experiments show that FS using IsBMFO improves the classification results. IsBMFO followed by support vector machine (SVM) classification is the best model for the SDP problem over other compared models, with an average G-mean of 78%.

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An intelligent feature selection approach based on moth flame optimization for medical diagnosis

Cited by 47 publications

References 45 publications

A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network

A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network

An Evolutionary Fake News Detection Method for COVID-19 Pandemic Information

An Enhanced Evolutionary Software Defect Prediction Method Using Island Moth Flame Optimization

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