Evolutionary Voting-Based Extreme Learning Machines

Voting-based extreme learning machine (V-ELM) was proposed to improve learning efficiency where majority voting was employed. V-ELM assumes that all individual classifiers contribute equally to the decision ensemble. However, in many real-world scenarios, this assumption does not work well. In this paper, we aim to enhance V-ELM by introducing weights to distinguish the importance of each individual ELM classifier in decision making. Genetic algorithm is used for optimizing these weights. This evolutionary V-ELM is named as EV-ELM. Results on several benchmark databases show that EV-ELM achieves the highest classification accuracy compared with V-ELM and ELM.

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An Expert Diagnosis System for Parkinson Disease Based on Genetic Algorithm-Wavelet Kernel-Extreme Learning Machine

Parkinson s Disease ◽

10.1155/2016/5264743 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 14

Author(s):

Derya Avci ◽

Akif Dogantekin

Keyword(s):

Genetic Algorithm ◽

Parkinson Disease ◽

Extreme Learning Machine ◽

Classification Accuracy ◽

Disease Diagnosis ◽

Major Public Health Problem ◽

Diagnosis System ◽

Kernel Extreme Learning Machine ◽

Learning Machine ◽

Hidden Neurons

Parkinson disease is a major public health problem all around the world. This paper proposes an expert disease diagnosis system for Parkinson disease based on genetic algorithm- (GA-) wavelet kernel- (WK-) Extreme Learning Machines (ELM). The classifier used in this paper is single layer neural network (SLNN) and it is trained by the ELM learning method. The Parkinson disease datasets are obtained from the UCI machine learning database. In wavelet kernel-Extreme Learning Machine (WK-ELM) structure, there are three adjustable parameters of wavelet kernel. These parameters and the numbers of hidden neurons play a major role in the performance of ELM. In this study, the optimum values of these parameters and the numbers of hidden neurons of ELM were obtained by using a genetic algorithm (GA). The performance of the proposed GA-WK-ELM method is evaluated using statical methods such as classification accuracy, sensitivity and specificity analysis, and ROC curves. The calculated highest classification accuracy of the proposed GA-WK-ELM method is found as 96.81%.

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Comparison of Several Extreme Learning Machine Algorithm for Modeling Concrete Compressive Strength

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.548-549.1735 ◽

2014 ◽

Vol 548-549 ◽

pp. 1735-1738 ◽

Cited By ~ 1

Author(s):

Jian Tang ◽

Dong Yan ◽

Li Jie Zhao

Keyword(s):

Compressive Strength ◽

Extreme Learning Machine ◽

Prediction Accuracy ◽

Partial Least Square ◽

Least Square ◽

Extreme Learning Machines ◽

Concrete Compressive Strength ◽

Learning Machines ◽

Learning Machine

Modeling concrete compressive strength is useful to ensure quality of civil engineering. This paper aims to compare several Extreme learning machines (ELMs) based modeling approaches for predicting the concrete compressive strength. Normal ELM algorithm, Partial least square-based extreme learning machines (PLS-ELMs) algorithm and Kernel ELM (KELM) algorithm are used and evaluated. Results indicate that the normal ELMs algorithm has the highest modeling speed, and the KELM has the best prediction accuracy. Every method is validated for modeling concrete compressive strength. The appropriate modeling approach should be selected according different purposes.

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Online Regularized and Kernelized Extreme Learning Machines with Forgetting Mechanism

Mathematical Problems in Engineering ◽

10.1155/2014/938548 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 7

Author(s):

Xinran Zhou ◽

Zijian Liu ◽

Congxu Zhu

Keyword(s):

Extreme Learning Machine ◽

Feedforward Neural Networks ◽

Matrix Inversion ◽

Time Varying ◽

Extreme Learning Machines ◽

Learning Machines ◽

Matrix Expansion ◽

Learning Machine ◽

Hidden Layer ◽

Forgetting Mechanism

To apply the single hidden-layer feedforward neural networks (SLFN) to identify time-varying system, online regularized extreme learning machine (ELM) with forgetting mechanism (FORELM) and online kernelized ELM with forgetting mechanism (FOKELM) are presented in this paper. The FORELM updates the output weights of SLFN recursively by using Sherman-Morrison formula, and it combines advantages of online sequential ELM with forgetting mechanism (FOS-ELM) and regularized online sequential ELM (ReOS-ELM); that is, it can capture the latest properties of identified system by studying a certain number of the newest samples and also can avoid issue of ill-conditioned matrix inversion by regularization. The FOKELM tackles the problem of matrix expansion of kernel based incremental ELM (KB-IELM) by deleting the oldest sample according to the block matrix inverse formula when samples occur continually. The experimental results show that the proposed FORELM and FOKELM have better stability than FOS-ELM and have higher accuracy than ReOS-ELM in nonstationary environments; moreover, FORELM and FOKELM have time efficiencies superiority over dynamic regression extreme learning machine (DR-ELM) under certain conditions.

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Classification of catenary equipment based on a coupled genetic algorithm-extreme learning machine method

Insight - Non-Destructive Testing and Condition Monitoring ◽

10.1784/insi.2020.62.1.15 ◽

2020 ◽

Vol 62 (1) ◽

pp. 15-21

Author(s):

Changdong Wu

Keyword(s):

Genetic Algorithm ◽

Extreme Learning Machine ◽

Monitoring System ◽

Classification Accuracy ◽

Activation Function ◽

Machine Method ◽

Learning Machine ◽

Hidden Layer ◽

Low Dimensional

In an online monitoring system for an electrified railway, it is important to classify the catenary equipment successfully. The extreme learning machine (ELM) is an effective image classification algorithm and the genetic algorithm (GA) is a typical optimisation method. In this paper, a coupled genetic algorithm-extreme learning machine (GA-ELM) technique is proposed for the classification of catenary equipment. Firstly, the GA is used to search for optimal features by reducing the initial multi-dimensional features to low-dimensional features. Next, the optimised features are used as the input to the ELM. The ELM algorithm is then used to classify the catenary equipment. In this process, the impacts of the activation function, the number of hidden layer neurons and different models on the performance of the ELM are discussed in turn. Finally, the proposed method is compared with traditional methods in terms of classification accuracy and efficiency. Experimental results show that the number of feature dimensions decreases to 58% of the original number and the computational complexity is greatly decreased. Moreover, the reduced features and the few steps of the ELM improve the classification accuracy and speed. Noticeably, when the performance of the GA-ELM method is compared with that of the ELM method, the classification accuracy rate is 93.33% compared with 85.83% and the time consumption is 2.25 s compared with 8.85 s, respectively. That is to say, the proposed method not only decreases the number of features but also increases the classification accuracy and efficiency. This meets the needs of a real-time online condition monitoring system.

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Computer assisted medical decision-making system using genetic algorithm and extreme learning machine for diagnosing allergic rhinitis

International Journal of Bio-Inspired Computation ◽

10.1504/ijbic.2020.10033327 ◽

2020 ◽

Vol 16 (3) ◽

pp. 148

Author(s):

A. Kannan ◽

H. Khanna Nehemiah ◽

Kindie Biredagn Nahato ◽

V.R. Elgin Christo ◽

J. Brighty

Keyword(s):

Genetic Algorithm ◽

Decision Making ◽

Allergic Rhinitis ◽

Extreme Learning Machine ◽

Medical Decision Making ◽

Medical Decision ◽

Computer Assisted ◽

Decision Making System ◽

Learning Machine

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An Effective Turkey Marble Classification System: Convolutional Neural Network with Genetic Algorithm -Wavelet Kernel - Extreme Learning Machine

Traitement du signal ◽

10.18280/ts.380434 ◽

2021 ◽

Vol 38 (4) ◽

pp. 1229-1235

Author(s):

Derya Avci ◽

Eser Sert

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Convolutional Neural Network ◽

Extreme Learning Machine ◽

Optical Illusions ◽

Factors Affecting ◽

Kernel Extreme Learning Machine ◽

Learning Machine ◽

Hidden Layer ◽

Elm Classifier

Marble is one of the most popular decorative elements. Marble quality varies depending on its vein patterns and color, which are the two most important factors affecting marble quality and class. The manual classification of marbles is likely to lead to various mistakes due to different optical illusions. However, computer vision minimizes these mistakes thanks to artificial intelligence and machine learning. The present study proposes the Convolutional Neural Network- (CNN-) with genetic algorithm- (GA) Wavelet Kernel- (WK-) Extreme Learning Machine (ELM) (CNN–GA-WK-ELM) approach. Using CNN architectures such as AlexNet, VGG-19, SqueezeNet, and ResNet-50, the proposed approach obtained 4 different feature vectors from 10 different marble images. Later, Genetic Algorithm (GA) was used to optimize adjustable parameters, i.e. k, 1, and m, and hidden layer neuron number in Wavelet Kernel (WK) – Extreme Learning Machine (ELM) and to increase the performance of ELM. Finally, 4 different feature vector parameters were optimized and classified using the WK-ELM classifier. The proposed CNN–GA-WK-ELM yielded an accuracy rate of 98.20%, 96.40%, 96.20%, and 95.60% using AlexNet, SequeezeNet, VGG-19, and ResNet-50, respectively.

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Spatial extreme learning machines: An application on prediction of disease counts

Statistical Methods in Medical Research ◽

10.1177/0962280218767985 ◽

2018 ◽

Vol 28 (9) ◽

pp. 2583-2594

Author(s):

Marcos O Prates

Keyword(s):

Learning Community ◽

Extreme Learning Machine ◽

Spatial Modeling ◽

Real Data ◽

Extreme Learning Machines ◽

Missing Information ◽

Learning Machines ◽

Uncertainty Prediction ◽

Learning Machine ◽

Many Sources

Extreme learning machines have gained a lot of attention by the machine learning community because of its interesting properties and computational advantages. With the increase in collection of information nowadays, many sources of data have missing information making statistical analysis harder or unfeasible. In this paper, we present a new model, coined spatial extreme learning machine, that combine spatial modeling with extreme learning machines keeping the nice properties of both methodologies and making it very flexible and robust. As explained throughout the text, the spatial extreme learning machines have many advantages in comparison with the traditional extreme learning machines. By a simulation study and a real data analysis we present how the spatial extreme learning machine can be used to improve imputation of missing data and uncertainty prediction estimation.

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Study on optimization of rake head density of suction hopper dredger based on bat algorithm and extreme learning machine

MATEC Web of Conferences ◽

10.1051/matecconf/202030904018 ◽

2020 ◽

Vol 309 ◽

pp. 04018

Author(s):

Guangjie Hao ◽

Menghong Yu ◽

Zhen Su

Keyword(s):

Extreme Learning Machine ◽

Prediction Models ◽

Prediction Method ◽

Bat Algorithm ◽

Yangtze Estuary ◽

Extreme Learning Machines ◽

Learning Machines ◽

Density Prediction ◽

Learning Machine ◽

Selection Of

The dredging output of suction dredger mainly comes from the suction density of the rake head. Accurate prediction of suction density is of great significance to improve the dredging output of suction dredger. In order to overcome the shortcomings of low accuracy and poor real-time performance of the current inhalation density prediction methods, a bat algorithm is proposed to optimize the inhalation density prediction method of extreme learning machine. The bat algorithms for optimizing extreme learning machines prediction model is constructed based on the measured construction data of “Xinhaifeng” Yangtze Estuary, and compared with other prediction models. Finally, the bat algorithms for optimizing extreme learning machines model is used to build the output simulator of inhalation density. Compared with the actual construction, the selection of control parameters is analyzed when the output of inhalation density is the best. Experients show that bat algorithms for optimizing extreme learning machines prediction has high accuracy and good stability, and can provide scientific and effective reference for yield prediction and construction guidance.

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Klasifikasi Keadaan Mata Berdasarkan sinyal EEG menggunakan Extreme Learning Machines

Indonesian Journal on Computing (Indo-JC) ◽

10.21108/indojc.2016.1.2.105 ◽

2016 ◽

Vol 1 (2) ◽

pp. 97 ◽

Cited By ~ 1

Author(s):

Ersa Christian Prakoso ◽

Untari Novia Wisesty ◽

Jondri .

Keyword(s):

Extreme Learning Machine ◽

Extreme Learning Machines ◽

White Space ◽

Font Size ◽

Stroke Width ◽

Learning Machines ◽

Normal Line ◽

Learning Machine

Electroencephalography atau sinyal EEG adalah salah satu biosignal yang marak menjadi topik penelitian saat ini. Sinyal EEG memiliki banyak manfaat seperti pendeteksian epilepsi, gangguan tidur, atau input dalam aplikasi komputer. Salah satu input yang dapat dideteksi berdasarkan sinyal EEG adalah keadaan mata. Namun untuk digunakan sebagai input dalam aplikasi diperlukan klasifikasi dengan performansi yang memadai. Oleh karena itu penulis akan dilakukan penelitian dimana salah satu metode pembelajaran Jaringan Syaraf Tiruan yaitu Extreme Learning Machine (ELM) akan diimplementasikan untuk mengklasifikasikan kondisi mata berdasarkan sinyal EEG. Dataset yang digunakan untuk melatih dan menguji model adalah dataset eye-state yang didonasikan oleh Oliver Roesler digabung dengan dataset yang berasal dari website repository Universitas of California, IrvineI (UCI) . Terdapat 7 corpus yang terdiri dari perekaman EEG yang dilakukan kepada 4 orang berbeda, lalu ditambahkan 1 corpus yang merupakan penggabungan seluruh corpus lain. Dari hasil pengujian yang dilakukan disimpulkan bahwa ELM dapat digunakan untuk klasifikasi keadaan mata dengan akurasi mencapai 97,95% dengan waktu latih hanya 0,81 detik jika masing-masing data digunakan secara terpisah, sedangkan penggabungan keseluruhan dataset hanya mencapai akurasi 78,94% dengan waktu latih 5,71 detik.<br style="font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: -webkit-auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px;" />

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Cholesky Factorization Based Online Sequential Extreme Learning Machines with Persistent Regularization and Forgetting Factor

Symmetry ◽

10.3390/sym11060801 ◽

2019 ◽

Vol 11 (6) ◽

pp. 801

Author(s):

Xinran Zhou ◽

Xiaoyan Kui

Keyword(s):

Extreme Learning Machine ◽

Cholesky Factorization ◽

Extreme Learning Machines ◽

Forgetting Factor ◽

Learning Machines ◽

Chunk Size ◽

Ill Posed ◽

Regression Problems ◽

Simulation Results ◽

Learning Machine

The online sequential extreme learning machine with persistent regularization and forgetting factor (OSELM-PRFF) can avoid potential singularities or ill-posed problems of online sequential regularized extreme learning machines with forgetting factors (FR-OSELM), and is particularly suitable for modelling in non-stationary environments. However, existing algorithms for OSELM-PRFF are time-consuming or unstable in certain paradigms or parameters setups. This paper presents a novel algorithm for OSELM-PRFF, named “Cholesky factorization based” OSELM-PRFF (CF-OSELM-PRFF), which recurrently constructs an equation for extreme learning machine and efficiently solves the equation via Cholesky factorization during every cycle. CF-OSELM-PRFF deals with timeliness of samples by forgetting factor, and the regularization term in its cost function works persistently. CF-OSELM-PRFF can learn data one-by-one or chunk-by-chunk with a fixed or varying chunk size. Detailed performance comparisons between CF-OSELM-PRFF and relevant approaches are carried out on several regression problems. The numerical simulation results show that CF-OSELM-PRFF demonstrates higher computational efficiency than its counterparts, and can yield stable predictions.

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