An improved binary sparrow search algorithm for feature selection in data classification

Gad, Ahmed G.; Sallam, Karam M.; Chakrabortty, Ripon K.; Ryan, Michael J.; Abohany, Amr A.

doi:10.1007/s00521-022-07203-7

Cited by 41 publications

(25 citation statements)

References 113 publications

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“…A fitness function sum of squared error (SSE) is also employed to demonstrate the algorithm's strength. We also compare the DL-MFA to five other popular algorithms that have been applied to feature selection in the past: BDASA [8], OBSSO [9], BGHO [10], IBSSA [11], and BBOA [12].…”

Section: Resultsmentioning

confidence: 99%

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A Discrete Moth Flame Algorithm for Feature Selection

2023

IJIES

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A crucial and important task in machine learning is feature selection (FS). The primary goal of the feature selection task is to minimize the dimension of the feature set while preserving performance accuracy. In order to address the FS task, a discrete moth flame algorithm that is combined with levy flights (DL-MFA) is presented in this research. The proposed DL-MFA imitates the natural navigational patterns of moths. The moths move along a straight line at a constant angle in the direction of the true light source (the moon) known as transverse orientation. Additionally, moths are drawn to artificial lights like fires and because of the close proximity; they constantly adjust their flying angles, creating a spiral path. In order to maintain healthy population diversity and increase the global search capabilities of the algorithm, the levy flight search technique is also used as a regulator of the moth position updating mechanism. The five swarm intelligence algorithms (SIAs) are contrasted with the proposed algorithm using measures such as entropy, purity, completeness score (CS), and homogeneity score (HS). For evaluating fitness, the SSE fitness function is utilised. The outcomes have shown that the proposed algorithm achieved purity values in the range 90% to 100%, and entropy 10% to 50%. Proposed DL-MFA has also achieved homogeneity score and completeness score up to 50%. These results prove that the proposed algorithm is better than its state-ofthe-art competitors.

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Section: Resultsmentioning

confidence: 99%

“…Hichem et al [10] them. Ahmed and colleagues [11] improved SSA by integrating a new local search and a method for repositioning the search agents (Sparrows) into the search space that are wandering beyond the search space. This improvement was carried out to enhance the searching efficiency of the original SSA.…”

Section: Related Workmentioning

confidence: 99%

A Discrete Moth Flame Algorithm for Feature Selection

2023

IJIES

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“…As iterations go, the bits at 1 will change to 0 to remove features and reduce the size of the subset Φ [34,57].…”

Section: Great Initializationmentioning

confidence: 99%

BHHO-EAS metaheuristic applied to the NP-Hard wrapper feature selection multi-objective optimization problem

SASSI,

CHELOUAH

2024

Preprint

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Faced with the increase in high-dimensional Big Data creating more volume and complexity, the feature selection process became an essential phase in the preprocessing workflow upstream of the design of systems based on deep learning. This paper is a concrete and first application of the new metaheuristic Harris Hawk Optimization Encirclement-Attack-Synergy (HHO-EAS) in solving the NP-Hard wrapper feature selection multi-objective optimization problem. This problem combines two contradictory objectives: maximizing the accuracy of a classifier while minimizing the number of the most relevant and non-redundant selected features. To do this we hybridized HHO-EAS to create the new metaheuristic Binary HHO-EAS (BHHO-EAS). We combined HHO-EAS to the sixteen transfer functions most used in the literature structured in a balanced way among the four main categories including S-Shaped, V-Shaped, Q-Shaped and U-Shaped. This wide range of transfer function allows us to analyze the evolution of BHHO-EAS’s skills according to the assigned transfer function and to determine which of them offer the best performances. We applied wrapper feature selection to the well-known NSL-KDD dataset with the deep learning Multi Layer Perceptron (MLP) classifier. We put BHHO-EAS in competition with three other well-known population based binary metaheuristics, BPSO, BBA and BHHO. The analysis of the experimental results, compared to the three other binary metaheuristics, demonstrated that BHHO-EAS obtained the best performance on 100% of the transfer functions. This is more particularly highlighted by the U-Shaped transfer functions, which give an acceptable compromise for the two objectives of the problem with an average accuracy of 96,4% and an average size of selected features of 20.

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“…This research suggests a hybrid sparrow-based TDOA/ AOA positioning method to improve outcomes in order to meet the strict standards for positioning accuracy in industrial applications. SSA has good performance in highdimensional function optimization [31], feature selection [32], and fault diagnosis [33]. This paper improves SSA algorithm and applies it to the TDOA/AOA localization problem for the first time and proposes a strategy for improving the location of sparrow finder by particle swarm.…”

Section: Introductionmentioning

confidence: 99%

A TDOA/AOA Hybrid Positioning Based on Improved Sparrow Search Algorithm for Mobile Position Estimation

et al. 2023

Wireless Communications and Mobile Computing

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To address the difficulty in calculating the nonlinear equation of time difference of arrival (TDOA) positioning, as well as the problem of measurement error in the hybrid time difference of arrival/angle of arrival (TDOA/AOA) positioning algorithm, an improved sparrow search algorithm is proposed to optimize positioning, and the optimization mechanism is retained on the basis of improving the performance of the original algorithm. The maximum likelihood estimation method is used to calculate the objective function, and then, the estimated function of the mobile station is used as the fitness function to generate the initial population of sparrows. Then, using particle swarm optimization, optimize the sparrow search algorithm and obtain the population’s optimal solution in order to obtain the optimal position. The simulation results show that, when compared to the existing algorithm, increasing the number of base stations increases the average accuracy of the sparrow search algorithm (SSA) positioning method by 18.54% and 4.5%, respectively, and, when compared to the proposed particle swarm optimization (PSO) positioning method, by 13.79% and 11.6% as the radius increases. The SSA hybrid positioning algorithm performs better in terms of positioning accuracy, convergence speed, and robustness.

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An improved binary sparrow search algorithm for feature selection in data classification

Cited by 41 publications

References 113 publications

A Discrete Moth Flame Algorithm for Feature Selection

A Discrete Moth Flame Algorithm for Feature Selection

BHHO-EAS metaheuristic applied to the NP-Hard wrapper feature selection multi-objective optimization problem

A TDOA/AOA Hybrid Positioning Based on Improved Sparrow Search Algorithm for Mobile Position Estimation

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