Two swarm intelligence approaches for tuning extreme learning machine

Al-Shamiri, Abobakr Khalil; Singh, Alok; Surampudi, Bapi Raju

doi:10.1007/s13042-017-0642-3

Cited by 16 publications

(16 citation statements)

References 25 publications

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“…One successful swarm intelligence application from this domain worth mentioning is presented in [47], where the implementation of a pair of swarm intelligence algorithms to tune the input weights and biases of ELM (ABC-ELM and IWO-ELM) are implemented. Ref.…”

Section: Machine Learning Model Tuning By Swarm Intelligence Metaheur...mentioning

confidence: 99%

Tuning Machine Learning Models Using a Group Search Firefly Algorithm for Credit Card Fraud Detection

Jovanovic¹,

Antonijevic

Stanković

et al. 2022

Mathematics

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Recent advances in online payment technologies combined with the impact of the COVID-19 global pandemic has led to a significant escalation in the number of online transactions and credit card payments being executed every day. Naturally, there has also been an escalation in credit card frauds, which is having a significant impact on the banking institutions, corporations that issue credit cards, and finally, the vendors and merchants. Consequently, there is an urgent need to implement and establish proper mechanisms that can secure the integrity of online card transactions. The research presented in this paper proposes a hybrid machine learning and swarm metaheuristic approach to address the challenge of credit card fraud detection. The novel, enhanced firefly algorithm, named group search firefly algorithm, was devised and then used to a tune support vector machine, an extreme learning machine, and extreme gradient-boosting machine learning models. Boosted models were tested on the real-world credit card fraud detection dataset, gathered from the transactions of the European credit card users. The original dataset is highly imbalanced; to further analyze the performance of tuned machine learning models, in the second experiment performed for the purpose of this research, the dataset has been expanded by utilizing the synthetic minority over-sampling approach. The performance of the proposed group search firefly metaheuristic was compared with other recent state-of-the-art approaches. Standard machine learning performance indicators have been used for the evaluation, such as the accuracy of the classifier, recall, precision, and area under the curve. The experimental findings clearly demonstrate that the models tuned by the proposed algorithm obtained superior results in comparison to other models hybridized with competitor metaheuristics.

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Section: Machine Learning Model Tuning By Swarm Intelligence Metaheur...mentioning

confidence: 99%

Tuning Machine Learning Models Using a Group Search Firefly Algorithm for Credit Card Fraud Detection

Jovanovic¹,

Antonijevic

Stanković

et al. 2022

Mathematics

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“…Extreme machine learning (ELM) represents one of the recent and promising approaches that can be applied to the single hidden layer feed-forward artificial neural networks (SLFN). This approach was initially proposed in [1], and it introduced the concept that the input weight and bias values in the hidden layer are allocated in a random fashion, while the output weight values are computed by utilizing the Moore-Penrose (MP) pseudo inverse [2]. ELMs have shown excellent generalization capabilities [3], and they are known to be very fast and efficient due to the fact that they do not require traditional training, which is one of the most time-consuming tasks when dealing with other types of neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…The process halts either when the maximal amount of neurons is met, or when the training rate starts decreasing. More recent research published by [2] proposed two swarm intelligence meta-heuristics to optimize the ELM, namely ELM-ABC (using the artificial bee colony meta-heuristics) and ELM-IWO (based on the invasive weed optimization method). Swarm meta-heuristics are used for tuning the input weight and bias parameters, while the ELM calculates the output weight values in the standard, analytical way.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Swarm Algorithm for Extreme Learning Machine Optimization

Bačanin

Stoean

Živković

et al. 2022

Sensors

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There are many machine learning approaches available and commonly used today, however, the extreme learning machine is appraised as one of the fastest and, additionally, relatively efficient models. Its main benefit is that it is very fast, which makes it suitable for integration within products that require models taking rapid decisions. Nevertheless, despite their large potential, they have not yet been exploited enough, according to the recent literature. Extreme learning machines still face several challenges that need to be addressed. The most significant downside is that the performance of the model heavily depends on the allocated weights and biases within the hidden layer. Finding its appropriate values for practical tasks represents an NP-hard continuous optimization challenge. Research proposed in this study focuses on determining optimal or near optimal weights and biases in the hidden layer for specific tasks. To address this task, a multi-swarm hybrid optimization approach has been proposed, based on three swarm intelligence meta-heuristics, namely the artificial bee colony, the firefly algorithm and the sine–cosine algorithm. The proposed method has been thoroughly validated on seven well-known classification benchmark datasets, and obtained results are compared to other already existing similar cutting-edge approaches from the recent literature. The simulation results point out that the suggested multi-swarm technique is capable to obtain better generalization performance than the rest of the approaches included in the comparative analysis in terms of accuracy, precision, recall, and f1-score indicators. Moreover, to prove that combining two algorithms is not as effective as joining three approaches, additional hybrids generated by pairing, each, two methods employed in the proposed multi-swarm approach, were also implemented and validated against four challenging datasets. The findings from these experiments also prove superior performance of the proposed multi-swarm algorithm. Sample code from devised ELM tuning framework is available on the GitHub.

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“…Likewise, the invasive weed optimization (IWO) proposed by Mehrabian and Lucas (2006) is inspired by colonizing behavior exhibited by weeds in an ecosystem. IWO algorithm too has been applied to solve a number of optimization problems (Venkatesh and Singh, 2015;Alshamiri et al, 2018;Safari et al, 2020;Liu and Nie, 2021). Motivated by the effectiveness demonstrated by ABC and IWO algorithms in solving numerous optimization problems, we have developed ABC and IWO algorithms based approaches to solve the MWDDS problem.…”

Section: Introductionmentioning

confidence: 99%