DCS-ELM: a novel method for extreme learning machine for regression problems and a new approach for the SFRSCC

Extreme learning machine (ELM) algorithm is widely used in regression and classification problems due to its advantages such as speed and high-performance rate. Different artificial intelligence-based optimization methods and chaotic systems have been proposed for the development of the ELM. However, a generalized solution method and success rate at the desired level could not be obtained. In this study, a new method is proposed as a result of developing the ELM algorithm used in regression problems with discrete-time chaotic systems. ELM algorithm has been improved by testing five different chaotic maps (Chebyshev, iterative, logistic, piecewise, tent) from chaotic systems. The proposed discrete-time chaotic systems based ELM (DCS-ELM) algorithm has been tested in steel fiber reinforced self-compacting concrete data sets and public four different datasets, and a result of its performance compared with the basic ELM algorithm, linear regression, support vector regression, kernel ELM algorithm and weighted ELM algorithm. It has been observed that it gives a better performance than other algorithms.

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Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification

BMC Bioinformatics ◽

10.1186/s12859-020-03790-1 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Liang-Rui Ren ◽

Ying-Lian Gao ◽

Jin-Xing Liu ◽

Junliang Shang ◽

Chun-Hou Zheng

Keyword(s):

Extreme Learning Machine ◽

High Performance ◽

Structural Information ◽

Optimization Method ◽

Classification Performance ◽

Weight Matrix ◽

New Method ◽

Classification Problems ◽

Graph Regularization ◽

Learning Machine

Abstract Background As a machine learning method with high performance and excellent generalization ability, extreme learning machine (ELM) is gaining popularity in various studies. Various ELM-based methods for different fields have been proposed. However, the robustness to noise and outliers is always the main problem affecting the performance of ELM. Results In this paper, an integrated method named correntropy induced loss based sparse robust graph regularized extreme learning machine (CSRGELM) is proposed. The introduction of correntropy induced loss improves the robustness of ELM and weakens the negative effects of noise and outliers. By using the L2,1-norm to constrain the output weight matrix, we tend to obtain a sparse output weight matrix to construct a simpler single hidden layer feedforward neural network model. By introducing the graph regularization to preserve the local structural information of the data, the classification performance of the new method is further improved. Besides, we design an iterative optimization method based on the idea of half quadratic optimization to solve the non-convex problem of CSRGELM. Conclusions The classification results on the benchmark dataset show that CSRGELM can obtain better classification results compared with other methods. More importantly, we also apply the new method to the classification problems of cancer samples and get a good classification effect.

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Extended Hierarchical Extreme Learning Machine with Multilayer Perceptron

ECTI Transactions on Computer and Information Technology (ECTI-CIT) ◽

10.37936/ecti-cit.2016102.68266 ◽

2017 ◽

Vol 10 (2) ◽

pp. 196-204

Author(s):

Khanittha Phumrattanaprapin ◽

Punyaphol Horata

Keyword(s):

Deep Learning ◽

Extreme Learning Machine ◽

Multilayer Perceptron ◽

High Performance ◽

Single Layer ◽

Classification Performance ◽

Classification Problems ◽

Hierarchical Learning ◽

Deep Architecture ◽

Learning Machine

The Deep Learning approach provides a high performance of classification, especially when invoking image classification problems. However, a shortcoming of the traditional Deep Learning method is the large time scale of training. The hierarchical extreme learning machine (H-ELM) framework was based on the hierarchical learning architecture of multilayer perceptron to address the problem. H-ELM is composed of two parts; the first entails unsupervised multilayer encoding, and the second is the supervised feature classification. H-ELM can give a higher accuracy rate than the traditional ELM. However, there still remains room to enhance its classification performance. This paper therefore proposes a new method termed the extending hierarchical extreme learning machine (EH-ELM), which extends the number of layers in the supervised portion of the H-ELM from a single layer to multiple layers. To evaluate the performance of the EH-ELM, the various classification datasets were studied and compared with the H-ELM and the multilayer ELM, as well as various state-of-the-art such deep architecture methods. The experimental results show that the EH-ELM improved the accuracy rates over most other methods.

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ELM Regularized Method for Classification Problems

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213015500268 ◽

2016 ◽

Vol 25 (01) ◽

pp. 1550026 ◽

Cited By ~ 7

Author(s):

Juan J. Carrasco ◽

Mónica Millán-Giraldo ◽

Juan Caravaca ◽

Pablo Escandell-Montero ◽

José M. Martínez-Martínez ◽

...

Keyword(s):

Extreme Learning Machine ◽

Single Layer ◽

Least Square ◽

Machine Learning Techniques ◽

Support Vector ◽

Problem Solution ◽

Classification Problems ◽

Training Time ◽

Learning Techniques ◽

Learning Machine

Extreme Learning Machine (ELM) is a recently proposed algorithm, efficient and fast for learning the parameters of single layer neural structures. One of the main problems of this algorithm is to choose the optimal architecture for a given problem solution. To solve this limitation several solutions have been proposed in the literature, including the regularization of the structure. However, to the best of our knowledge, there are no works where such adjustment is applied to classification problems in the presence of a non-linearity in the output; all published works tackle modelling or regression problems. Our proposal has been applied to a series of standard databases for the evaluation of machine learning techniques. Results obtained in terms of classification success rate and training time, are compared to the original ELM, to the well known Least Square Support Vector Machine (LS-SVM) algorithm and with two other methods based on the ELM regularization: Optimally Pruned Extreme Learning Machine (OP-ELM) and Bayesian Extreme Learning Machine (BELM). The obtained results clearly demonstrate the usefulness of the proposed method and its superiority over a classical approach.

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An efficient computational method for predicting drug-target interactions using weighted extreme learning machine and speed up robot features

BioData Mining ◽

10.1186/s13040-021-00242-1 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Ji-Yong An ◽

Fan-Rong Meng ◽

Zi-Ji Yan

Keyword(s):

Ion Channel ◽

Extreme Learning Machine ◽

Nuclear Receptor ◽

Drug Target ◽

Computational Method ◽

Evolutionary Information ◽

Support Vector ◽

Weighted Extreme Learning Machine ◽

Speed Up ◽

Learning Machine

Abstract Background Prediction of novel Drug–Target interactions (DTIs) plays an important role in discovering new drug candidates and finding new proteins to target. In consideration of the time-consuming and expensive of experimental methods. Therefore, it is a challenging task that how to develop efficient computational approaches for the accurate predicting potential associations between drug and target. Results In the paper, we proposed a novel computational method called WELM-SURF based on drug fingerprints and protein evolutionary information for identifying DTIs. More specifically, for exploiting protein sequence feature, Position Specific Scoring Matrix (PSSM) is applied to capturing protein evolutionary information and Speed up robot features (SURF) is employed to extract sequence key feature from PSSM. For drug fingerprints, the chemical structure of molecular substructure fingerprints was used to represent drug as feature vector. Take account of the advantage that the Weighted Extreme Learning Machine (WELM) has short training time, good generalization ability, and most importantly ability to efficiently execute classification by optimizing the loss function of weight matrix. Therefore, the WELM classifier is used to carry out classification based on extracted features for predicting DTIs. The performance of the WELM-SURF model was evaluated by experimental validations on enzyme, ion channel, GPCRs and nuclear receptor datasets by using fivefold cross-validation test. The WELM-SURF obtained average accuracies of 93.54, 90.58, 85.43 and 77.45% on enzyme, ion channels, GPCRs and nuclear receptor dataset respectively. We also compared our performance with the Extreme Learning Machine (ELM), the state-of-the-art Support Vector Machine (SVM) on enzyme and ion channels dataset and other exiting methods on four datasets. By comparing with experimental results, the performance of WELM-SURF is significantly better than that of ELM, SVM and other previous methods in the domain. Conclusion The results demonstrated that the proposed WELM-SURF model is competent for predicting DTIs with high accuracy and robustness. It is anticipated that the WELM-SURF method is a useful computational tool to facilitate widely bioinformatics studies related to DTIs prediction.

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Pressure Prediction of Coal Slurry Transportation Pipeline Based on Particle Swarm Optimization Kernel Function Extreme Learning Machine

Mathematical Problems in Engineering ◽

10.1155/2015/542182 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7

Author(s):

Xue-cun Yang ◽

Xiao-ru Yan ◽

Chun-feng Song

Keyword(s):

Support Vector Machine ◽

Prediction Model ◽

Extreme Learning Machine ◽

Kernel Function ◽

Particle Swarm ◽

Support Vector ◽

Particle Swarm Algorithm ◽

Coal Slurry ◽

Swarm Optimization ◽

Learning Machine

For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM) is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM) and kernel function extreme learning machine prediction model (KELM). The results prove that mean square error (MSE) for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.

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Multi-Classification of Fetal Health Status Using Extreme Learning Machine

ICONTECH INTERNATIONAL JOURNAL ◽

10.46291/icontechvol5iss2pp62-70 ◽

2021 ◽

Vol 5 (2) ◽

pp. 62-70

Author(s):

Ömer KASIM

Keyword(s):

Extreme Learning Machine ◽

Classification Accuracy ◽

Binary Classification ◽

Clinical Decision ◽

Support Vector ◽

Data Set ◽

Multiple Classification ◽

Learning Machine ◽

Multi Class Classification ◽

The University

Cardiotocography (CTG) is used for monitoring the fetal heart rate signals during pregnancy. Evaluation of these signals by specialists provides information about fetal status. When a clinical decision support system is introduced with a system that can automatically classify these signals, it is more sensitive for experts to examine CTG data. In this study, CTG data were analysed with the Extreme Learning Machine (ELM) algorithm and these data were classified as normal, suspicious and pathological as well as benign and malicious. The proposed method is validated with the University of California International CTG data set. The performance of the proposed method is evaluated with accuracy, f1 score, Cohen kappa, precision, and recall metrics. As a result of the experiments, binary classification accuracy was obtained as 99.29%. There was only 1 false positive. When multi-class classification was performed, the accuracy was obtained as 98.12%. The amount of false positives was found as 2. The processing time of the training and testing of the ELM algorithm were quite minimized in terms of data processing compared to the support vector machine and multi-layer perceptron. This result proved that a high classification accuracy was obtained by analysing the CTG data both binary and multiple classification.

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Risk Stratification with Extreme Learning Machine: A Retrospective Study on Emergency Department Patients

Mathematical Problems in Engineering ◽

10.1155/2014/248938 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 3

Author(s):

Nan Liu ◽

Jiuwen Cao ◽

Zhi Xiong Koh ◽

Pin Pin Pek ◽

Marcus Eng Hock Ong

Keyword(s):

Emergency Department ◽

Risk Stratification ◽

Extreme Learning Machine ◽

Tertiary Hospital ◽

Support Vector ◽

Characteristic Analysis ◽

Scoring Method ◽

Stratification Method ◽

Emergency Department Patients ◽

Learning Machine

This paper presents a novel risk stratification method using extreme learning machine (ELM). ELM was integrated into a scoring system to identify the risk of cardiac arrest in emergency department (ED) patients. The experiments were conducted on a cohort of 1025 critically ill patients presented to the ED of a tertiary hospital. ELM and voting based ELM (V-ELM) were evaluated. To enhance the prediction performance, we proposed a selective V-ELM (SV-ELM) algorithm. The results showed that ELM based scoring methods outperformed support vector machine (SVM) based scoring method in the receiver operation characteristic analysis.

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Short-Term Traffic Flow Forecasting Based on Data-Driven Model

Mathematics ◽

10.3390/math8020152 ◽

2020 ◽

Vol 8 (2) ◽

pp. 152 ◽

Cited By ~ 2

Author(s):

Su-qi Zhang ◽

Kuo-Ping Lin

Keyword(s):

Support Vector Machine ◽

Traffic Flow ◽

Extreme Learning Machine ◽

Support Vector ◽

Local Optimum ◽

Short Term ◽

Swarm Optimization ◽

Traffic Flow Forecasting ◽

Learning Machine ◽

The One

Short-term traffic flow forecasting is the technical basis of the intelligent transportation system (ITS). Higher precision, short-term traffic flow forecasting plays an important role in alleviating road congestion and improving traffic management efficiency. In order to improve the accuracy of short-term traffic flow forecasting, an improved bird swarm optimizer (IBSA) is used to optimize the random parameters of the extreme learning machine (ELM). In addition, the improved bird swarm optimization extreme learning machine (IBSAELM) model is established to predict short-term traffic flow. The main researches in this paper are as follows: (1) The bird swarm optimizer (BSA) is prone to fall into the local optimum, so the distribution mechanism of the BSA optimizer is improved. The first five percent of the particles with better fitness values are selected as producers. The last ten percent of the particles with worse fitness values are selected as beggars. (2) The one-day and two-day traffic flows are predicted by the support vector machine (SVM), particle swarm optimization support vector machine (PSOSVM), bird swarm optimization extreme learning machine (BSAELM) and IBSAELM models, respectively. (3) The prediction results of the models are evaluated. For the one-day traffic flow sequence, the mean absolute percentage error (MAPE) values of the IBSAELM model are smaller than the SVM, PSOSVM and BSAELM models, respectively. The experimental analysis results show that the IBSAELM model proposed in this study can meet the actual engineering requirements.

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