Binary whale optimisation: an effective swarm algorithm for feature selection

Eid, Heba F.

doi:10.1504/ijmheur.2018.091880

Cited by 41 publications

(16 citation statements)

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“…The WOA algorithm in its original form is for continuous optimization; however, many problems are formulated as mixed-integer programming (MIP) problems, where each variable value can be discrete or binary. To deal with the combinational optimization, binary versions of the WOA algorithm have been proposed [13], [14]. The pseudocode of the BWOA algorithm is illustrated in Alg.…”

Section: Binary Whale Optimization Algorithmmentioning

confidence: 99%

Whale Optimization Algorithm With Applications to Resource Allocation in Wireless Networks

Pham¹,

Mirjalili²,

Kumar³

et al. 2020

IEEE Trans. Veh. Technol.

244

101

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Section: Binary Whale Optimization Algorithmmentioning

confidence: 99%

Whale Optimization Algorithm With Applications to Resource Allocation in Wireless Networks

Pham¹,

Mirjalili²,

Kumar³

et al. 2020

IEEE Trans. Veh. Technol.

244

101

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show abstract

“…In addition, it was used as the index to evaluate the stability of the algorithm in this experiment. Std is formulated as in Equation (25).…”

Section: The Effect Of Adaptive Restart Approach On the Proposed Algomentioning

confidence: 99%

“…The binary version of meta-heuristic algorithms have a positive performance in solving binary optimization problems. Thus, there are many different binary meta-heuristic algorithms have been proposed to solve the problem of feature selection and achieved excellent results [ 23 , 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Electronic Nose Performance by Feature Selection Using an Improved Grey Wolf Optimization Based Algorithm

Zhang

Wang

Pan³

2020

Sensors

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Electronic nose is a kind of widely-used artificial olfactory system for the detection and classification of volatile organic compounds. The high dimensionality of data collected by electronic noses can hinder the process of pattern recognition. Thus, the feature selection is an essential stage in building a robust and accurate model for gas recognition. This paper proposed an improved grey wolf optimizer (GWO) based algorithm for feature selection and applied it on electronic nose data for the first time. Two mechanisms are employed for the proposed algorithm. The first mechanism contains two novel binary transform approaches, which are used for searching feature subset from electronic nose data that maximizing the classification accuracy while minimizing the number of features. The second mechanism is based on the adaptive restart approach, which attempts to further enhance the search capability and stability of the algorithm. The proposed algorithm is compared with five efficient feature selection algorithms on three electronic nose data sets. Three classifiers and multiple assessment indicators are used to evaluate the performance of algorithm. The experimental results show that the proposed algorithm can effectively select the feature subsets that are conducive to gas recognition, which can improve the performance of the electronic nose.

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“…Mafarja et al used mutation operator sigmoid and V-shaped transfer functions to improve the exploration quality of BGOA [36]. To solve the binary optimization problems, Eid et al [37] added transformation functions to the conventional whale optimization algorithm (WOA). The exploitation capability of WOA has been improved by Mafarja et al [38], he integrated the simulated annealing into WOA in each iteration step to enhance the best solution.…”

Section: Introductionmentioning

confidence: 99%

An Improved Equilibrium Optimizer Algorithm for Features Selection: Methods and Analysis

Elmanakhly¹,

Saleh²,

Rashed³

2021

IEEE Access

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In the last decade, data generated from different digital devices has posed a remarkable challenge for data representation and analysis. Because of the high-dimensional datasets and the rapid growth of data volume, a lot of challenges have been encountered in various fields such as data mining and data science. Conventional machine learning classifiers are of limited ability to handle the problems of high dimensionality that includes memory limitation, computational cost, and low accuracy performance. Consequently, there is a need to reduce the dimension of datasets by choosing the most significant features that would represent the data efficiently with minimum volume. This study proposes an improved binary version of the equilibrium optimizer algorithm (IBEO) to mitigate features selection problem. Two main enhancements are added to the original equilibrium optimizer (EO) to strengthen its performance. Opposition based learning is the first advancement added to the initialization stage of EO to enhance the diversity of the population in the search space. Local search algorithm is the second advancement added to enhance the exploitation of EO. Wrapper approaches can offer premium solutions. Thus, we used k-nearest neighbour classifier and support vector machine classifiers as the most popular wrapper methods. Moreover, dealing with the problem of over-fitting is an essential task that urges on applying k-fold cross-validation to split each dataset into training and testing data. Comparative tests with different well-known algorithms such as grey wolf optimization, grasshopper optimization, particle swarm optimization, whale optimization, dragonfly, and improved salp swarm algorithms are considered. The proposed algorithm is applied to the most commonly datasets used in the field to validate the performance. Statistical analysis studies demonstrate the effectiveness of the IBEO.

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Binary whale optimisation: an effective swarm algorithm for feature selection

Cited by 41 publications

References 0 publications

Whale Optimization Algorithm With Applications to Resource Allocation in Wireless Networks

Whale Optimization Algorithm With Applications to Resource Allocation in Wireless Networks

Enhancing Electronic Nose Performance by Feature Selection Using an Improved Grey Wolf Optimization Based Algorithm

An Improved Equilibrium Optimizer Algorithm for Features Selection: Methods and Analysis

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