A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training

Yang, Zhiyong; Zhang, Taohong; Zhang, Dezheng

doi:10.1007/s11571-015-9358-9

Cited by 33 publications

(11 citation statements)

References 29 publications

(20 reference statements)

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“…In a suitable time -period of computation, more optimal solutions are built with the hybrid evolutionary algorithm framework by approximating the machine learning fitness. Genetic parameters will be used to carry the searching process out and local search strategy will be utilized to refine the best solution in the population [38,39].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Review of Optimization in Improving Extreme Learning Machine

Rathod¹,

Wankhade²

2021

EAI Endorsed Transactions on Industrial Networks and Intelligen

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Now a days Extreme Learning Machine has gained a lot of interest because of its noteworthy qualities over single hiddenlayer feedforward neural networks and the kernel functions. Even if ELM has many advantages, it has some potential shortcomings such as performance sensitivity to the underlying state of the hidden neurons, input weights and the choice of functions of activation. To overcome the limitations of traditional ELM, analysts have devised numerical methods to optimise specific parts of ELM in order to enhance ELM performance for a variety of complicated difficulties and applications. Hence through this study, we intend to study the different algorithms developed for optimizing the ELM to enhance its performance in the aspects of survey criteria such as datasets, algorithm, objectives, training time, accuracy, error rate and the hidden neurons. This study will help other researchers to find out the research issues that lowering the performance of the ELM.

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Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Review of Optimization in Improving Extreme Learning Machine

Rathod¹,

Wankhade²

2021

EAI Endorsed Transactions on Industrial Networks and Intelligen

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“…Therefore, to enhance the conditioning of ELM and to guarantee the optimal solutions, evolutionary algorithms, namely, genetic algorithm (GA) [18], particle swarm optimisation (PSO) [19], and differential evolution (DE) [20] have been embedded with ELM. Some of the evolutionary ELM methods, namely, GA‐based ELM [21], PSO‐based ELM [22], DE‐based ELM [23], artificial bee colony(ABC)‐based ELM [24], and cuckoo search‐based ELM [15] have been developed in the literature in the recent past.…”

Section: Introductionmentioning

confidence: 99%

Chaotic emperor penguin optimised extreme learning machine for microarray cancer classification

Baliarsingh

Vipsita

2020

IET syst. biol.

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“…The literature addresses the problem of optimizing the weights of the single-hidden layer feedforward neural networks (SLFN) trained by the ELM using various approaches. Researchers [ 13 , 14 ] attempted to optimize the weights using meta-heuristic searching methods. Also, [ 15 ] aimed to optimize the weights of the ELM using the teaching phase and the learning phase under the ameliorated teaching learning-based optimisation framework.…”

Section: Introductionmentioning

confidence: 99%

Spoken language identification based on the enhanced self-adjusting extreme learning machine approach

et al. 2018

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Spoken Language Identification (LID) is the process of determining and classifying natural language from a given content and dataset. Typically, data must be processed to extract useful features to perform LID. The extracting features for LID, based on literature, is a mature process where the standard features for LID have already been developed using Mel-Frequency Cepstral Coefficients (MFCC), Shifted Delta Cepstral (SDC), the Gaussian Mixture Model (GMM) and ending with the i-vector based framework. However, the process of learning based on extract features remains to be improved (i.e. optimised) to capture all embedded knowledge on the extracted features. The Extreme Learning Machine (ELM) is an effective learning model used to perform classification and regression analysis and is extremely useful to train a single hidden layer neural network. Nevertheless, the learning process of this model is not entirely effective (i.e. optimised) due to the random selection of weights within the input hidden layer. In this study, the ELM is selected as a learning model for LID based on standard feature extraction. One of the optimisation approaches of ELM, the Self-Adjusting Extreme Learning Machine (SA-ELM) is selected as the benchmark and improved by altering the selection phase of the optimisation process. The selection process is performed incorporating both the Split-Ratio and K-Tournament methods, the improved SA-ELM is named Enhanced Self-Adjusting Extreme Learning Machine (ESA-ELM). The results are generated based on LID with the datasets created from eight different languages. The results of the study showed excellent superiority relating to the performance of the Enhanced Self-Adjusting Extreme Learning Machine LID (ESA-ELM LID) compared with the SA-ELM LID, with ESA-ELM LID achieving an accuracy of 96.25%, as compared to the accuracy of SA-ELM LID of only 95.00%.

show abstract

A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training

Cited by 33 publications

References 29 publications

Review of Optimization in Improving Extreme Learning Machine

Review of Optimization in Improving Extreme Learning Machine

Chaotic emperor penguin optimised extreme learning machine for microarray cancer classification

Spoken language identification based on the enhanced self-adjusting extreme learning machine approach

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