Trends in extreme learning machines: A review

Huang, Gao; Huang, Guang-Bin; Song, Shiji; You, Keyou

doi:10.1016/j.neunet.2014.10.001

Cited by 1,553 publications

(711 citation statements)

References 178 publications

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“…Studies using ELM have yielded very fast learning times with good generalization performance due to the fact that the ELM simplifies the entire training process [HUANG et al 2004;LIANG et al 2006;MOHAMMADI et al 2015a, b;SHAMSHIRBAND et al 2015a, b]. A recent study by HUANG et al [2015] further showed that the ELM algorithm overcomes the problem of slow learning speed associated with traditional methods such as the back-propagation method and yields a better performance due to its ability to obtain the smallest training error and norm of weight. Thus, the ELM algorithm has gained popularity in various scientific fields such as the forecasting of coal mine water inrush [ZHAO et al 2013], non-stationary time series prediction [WANG, HAN 2014], estimation of monsoon rainfall [ACHARYA et al 2014], estimation of wind speed distribution , and sales forecasting [SUN et al 2008].…”

Section: Previous Researchmentioning

confidence: 99%

Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction

Yadav¹,

Ch²,

Mathur³

et al. 2017

Journal of Water and Land Development

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Fluctuation of groundwater levels around the world is an important theme in hydrological research. Rising water demand, faulty irrigation practices, mismanagement of soil and uncontrolled exploitation of aquifers are some of the reasons why groundwater levels are fluctuating. In order to effectively manage groundwater resources, it is important to have accurate readings and forecasts of groundwater levels. Due to the uncertain and complex nature of groundwater systems, the development of soft computing techniques (data-driven models) in the field of hydrology has significant potential. This study employs two soft computing techniques, namely, extreme learning machine (ELM) and support vector machine (SVM) to forecast groundwater levels at two observation wells located in Canada. A monthly data set of eight years from 2006 to 2014 consisting of both hydrological and meteorological parameters (rainfall, temperature, evapotranspiration and groundwater level) was used for the comparative study of the models. These variables were used in various combinations for univariate and multivariate analysis of the models. The study demonstrates that the proposed ELM model has better forecasting ability compared to the SVM model for monthly groundwater level forecasting.

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Section: Previous Researchmentioning

confidence: 99%

Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction

Yadav¹,

Ch²,

Mathur³

et al. 2017

Journal of Water and Land Development

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show abstract

“…Unlike backpropagation, ELM is not only aims to achieve a minimum error learning, but also the smallest norm of weight. The function of the output of ELM is (Huang et al, 2015): (1) where is the output weight between hidden layer nodes and output nodes, and is the ELM nonlinear feature mapping (Figure 2), where is the output of the i-th node to the hidden layer. The output function in hidden nodes may use a different activation function for each node.…”

Section: Methodsology Extreme Learning Machinementioning

confidence: 99%

“…ELM has been gaining high attention from researchers since its announcement (Huang et al, 2015). It is not only researched within the scope of classification problems, but also in the scope of regression and clustering problems.…”

Section: Introductionmentioning

confidence: 99%

Modification of Hidden Layer Weight in Extreme Learning Machine Using Gain Ratio

Anggraeny¹,

Purbasari²

2016

MATEC Web of Conferences

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Extreme Learning Machine (ELM) is a method of learning feed forward neural network quickly and has a fairly good accuracy. This method is devoted to a feed forward neural network with one hidden layer where the parameters (i.e. weight and bias) are adjusted one time randomly at the beginning of the learning process. In neural network, the input layer is connected to all characteristics/features, and the output layer is connected to all classes of species. This research used three datasets from UCI database, which were Iris, Breast Wisconsin, and Dermatology, with each dataset having several features. Each characteristic/feature of the data has a role in the process of classification levels, starting from the most influencing role to non-influencing at all. Gain ratio was used to extract each feature role on each datasets. Gain ratio is a method to extract feature role in order to develop a decision tree structure. In this study, ELM structure has been modified, where the random weights of the hidden layer were adjusted to the level of each feature role in determining the species class, so as to improve the level of training and testing accuracy. The proposed method has higher classification accuracy rate than basic ELM on all three datasets, which were 99%, 96%, and 82%, respectively.

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“…Recently, ELMs have been widely used in fields such as computer vision, biomedical engineering, and control and robotics, because they are simple, efficient and have impressive performance [18], [19] [20] and [21]. ELMs have single layer hidden node parameters which are randomly generated.…”

Section: B Identifying Speakers Using Extreme Learning Machinementioning

confidence: 99%

Comparison of I-vector and GMM-UBM approaches to speaker identification with TIMIT and NIST 2008 databases in challenging environments

Al-Kaltakchi

Woo

Dlay

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper, two models, the I-vector and the Gaussian Mixture Model-Universal Background Model (GMM-UBM), are compared for the speaker identification task. Four feature combinations of I-vectors with seven fusion techniques are considered: maximum, mean, weighted sum, cumulative, interleaving and concatenated for both two and four features. In addition, an Extreme Learning Machine (ELM) is exploited to identify speakers, and then Speaker Identification Accuracy (SIA) is calculated. Both systems are evaluated for 120 speakers from the TIMIT and NIST 2008 databases for clean speech. Furthermore, a comprehensive evaluation is made under Additive White Gaussian Noise (AWGN) conditions and with three types of Non Stationary Noise (NSN), both with and without handset effects for the TIMIT database. The results show that the Ivector approach is better than the GMM-UBM for both clean and AWGN conditions without a handset. However, the GMM-UBM had better accuracy for NSN types.

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Trends in extreme learning machines: A review

Cited by 1,553 publications

References 178 publications

Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction

Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction

Modification of Hidden Layer Weight in Extreme Learning Machine Using Gain Ratio

Comparison of I-vector and GMM-UBM approaches to speaker identification with TIMIT and NIST 2008 databases in challenging environments

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