Hybrid binary bat enhanced particle swarm optimization algorithm for solving feature selection problems

Tawhid, Mohamed A.; Dsouza, Kevin Bradley

doi:10.1016/j.aci.2018.04.001

Cited by 91 publications

(50 citation statements)

References 30 publications

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“…Also, we would like to apply our proposed algorithm on solving unconstrained optimization problems [1], large scale problems and molecular potential energy function [42], [41], team formation problem [8], and minimax and integer programming problems [2], [39], [40]. Furthermore, we would like to use binary version to solve feature selection problems [37], [38].…”

Section: Discussionmentioning

confidence: 99%

An effective hybrid firefly algorithm with the cuckoo search for engineering optimization problems

Tawhid¹,

Ali²

2018

Mathematical Foundations of Computing

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Firefly and cuckoo search algorithms are two of the most widely used nature-inspired algorithms due to their simplicity and inexpensive computational cost when they applied to solve a wide range of problems. In this article, a new hybrid algorithm is suggested by combining the firefly algorithm and the cuckoo search algorithm to solve constrained optimization problems (COPs) and real-world engineering optimization problems. The proposed algorithm is called Hybrid FireFly Algorithm and Cuckoo Search (HFFACS) algorithm. In the HFFACS algorithm, a balance between the exploration and the exploitation processes is considered. The main drawback of the firefly algorithm is it is easy to fall into stagnation when the new solution is not better than its previous best solution for several generations. In order to avoid this problem, the cuckoo search with Lèvy flight is invoked to force the firefly algorithm to escape from stagnation and to avoid premature convergence. The proposed algorithm is applied to six benchmark constrained optimization problems and five engineering optimization problems and compared against four algorithms to investigate its performance. The numerical experimental results show the proposed algorithm is a promising algorithm and can obtain the optimal or near optimal solution within a reasonable time.

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Section: Discussionmentioning

confidence: 99%

An effective hybrid firefly algorithm with the cuckoo search for engineering optimization problems

Tawhid¹,

Ali²

2018

Mathematical Foundations of Computing

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“…For fitness evaluation, the k-nearest neighbor (KNN) with a Euclidean distance and k = 1 was used as the learning algorithm. The KNN was chosen because it is a common, fast, and simple machine learning algorithm that has been widely applied in feature selection studies [33,34]. For performance evaluation, the 10-fold cross validation method was implemented.…”

Section: Application Of Bpsode For Feature Selectionmentioning

confidence: 99%

Hybrid Binary Particle Swarm Optimization Differential Evolution-Based Feature Selection for EMG Signals Classification

Too

Abdullah

Saad

2019

Axioms

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To date, the usage of electromyography (EMG) signals in myoelectric prosthetics allows patients to recover functional rehabilitation of their upper limbs. However, the increment in the number of EMG features has been shown to have a great impact on performance degradation. Therefore, feature selection is an essential step to enhance classification performance and reduce the complexity of the classifier. In this paper, a hybrid method, namely, binary particle swarm optimization differential evolution (BPSODE) was proposed to tackle feature selection problems in EMG signals classification. The performance of BPSODE was validated using the EMG signals of 10 healthy subjects acquired from a publicly accessible EMG database. First, discrete wavelet transform was applied to decompose the signals into wavelet coefficients. The features were then extracted from each coefficient and formed into the feature vector. Afterward, BPSODE was used to evaluate the most informative feature subset. To examine the effectiveness of the proposed method, four state-of-the-art feature selection methods were used for comparison. The parameters, including accuracy, feature selection ratio, precision, F-measure, and computation time were used for performance measurement. Our results showed that BPSODE was superior, in not only offering a high classification performance, but also in having the smallest feature size. From the empirical results, it can be inferred that BPSODE-based feature selection is useful for EMG signals classification.

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“…The performance of proposed method was estimated on two benchmarks and three clinical microarrays. Tawhid and Dsouza investigated a hybrid binary bat‐enhanced PSO algorithm for gene subset selection on the high‐dimensional datasets. A set of assessment indicators were used to assess and compare the different methods over standard datasets.…”

Section: Literature Workmentioning

confidence: 99%

Identification of cancerous gene groups from microarray data by employing adaptive genetic and support vector machine technique

Shukla¹

2019

Computational Intelligence

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Nowadays, microarray gene expression data plays a vital role in tumor classification. However, due to the accessibility of a limited number of tissues compared to large number of genes in genomic data, various existing methods have failed to identify a small subset of discriminative genes. To overcome this limitation, in this paper, we developed a new hybrid technique for gene selection, called ensemble multipopulation adaptive genetic algorithm (EMPAGA) that can overlook the irrelevant genes and classify cancer accurately. The proposed hybrid gene selection algorithm comprises of two phase. In the first phase, an ensemble gene selection (EGS) method used to filter the noisy and redundant genes in high‐dimensional datasets by combining multilayer and F‐score approaches. Then, an adaptive genetic algorithm based on multipopulation strategy with support vector machine and naïve Bayes (NB) classifiers as a fitness function is applied for gene selection to select the extremely sensible genes from the reduced datasets. The performance of the proposed method is estimated on 10 microarray datasets of numerous tumor. The comprehensive results and various comparisons disclose that EGS has a remarkable impact on the efficacy of the adaptive genetic algorithm with multipopulation strategy and enhance the capability of the proposed approach in terms of convergence rate and solution quality. The experiments results demonstrate the superiority of the proposed method when compared to other standard wrappers regarding classification accuracy and optimal number of genes.

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Hybrid binary bat enhanced particle swarm optimization algorithm for solving feature selection problems

Cited by 91 publications

References 30 publications

An effective hybrid firefly algorithm with the cuckoo search for engineering optimization problems

An effective hybrid firefly algorithm with the cuckoo search for engineering optimization problems

Hybrid Binary Particle Swarm Optimization Differential Evolution-Based Feature Selection for EMG Signals Classification

Identification of cancerous gene groups from microarray data by employing adaptive genetic and support vector machine technique

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