A Hybrid Kernel Extreme Learning Machine and Improved Cat Swarm Optimization for Microarray Medical Data Classification

This paper presents the pattern classification of the binary microarray gene expression based medical data using extreme learning machine (ELM) and its variants like on-line sequential ELM (OSELM) and kernel based extreme learning machine (KELM). In the KELM category two variants namely the wavelet based kernel (WKELM) extreme learning machine and radial basis kernel extreme learning machine (RKELM) along with support vector machine (SVMRBF) and support vector machine polynomial (SVMPoly) are used to classify microarray medical datasets. Further to reduce the high dimensionality of Microarray medical datasets giving rise to high number of gene expression and small sample sizes, a modified evolutionary cat swarm optimization (MCSO) technique is adopted. The efficiency of the proposed algorithm is verified using a set of performance metrics for four binary medical datasets belonging to breast cancer, prostate cancer, colon tumor, and leukemia, respectively.

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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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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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Prediction of ozone hourly concentrations by support vector machine and kernel extreme learning machine using wavelet transformation and partial least squares methods

Atmospheric Pollution Research ◽

10.1016/j.apr.2020.02.024 ◽

2020 ◽

Vol 11 (6) ◽

pp. 51-60 ◽

Cited By ~ 1

Author(s):

Xiaoqian Su ◽

Junlin An ◽

Yuxin Zhang ◽

Ping Zhu ◽

Bin Zhu

Keyword(s):

Support Vector Machine ◽

Least Squares ◽

Partial Least Squares ◽

Extreme Learning Machine ◽

Wavelet Transformation ◽

Support Vector ◽

Kernel Extreme Learning Machine ◽

Learning Machine

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Wheel/Rail Adhesion State Identification of Heavy-Haul Locomotive Based on Particle Swarm Optimization and Kernel Extreme Learning Machine

Journal of Advanced Transportation ◽

10.1155/2020/8136939 ◽

2020 ◽

Vol 2020 ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Jianhua Liu ◽

Linfan Liu ◽

Jing He ◽

Changfan Zhang ◽

Kaihui Zhao

Keyword(s):

Particle Swarm Optimization ◽

Extreme Learning Machine ◽

Particle Swarm ◽

Support Vector ◽

Swarm Optimization ◽

State Identification ◽

Kernel Parameter ◽

Heavy Haul ◽

Kernel Extreme Learning Machine ◽

Learning Machine

The traction performance of heavy-haul locomotive is subject to the wheel/rail adhesion states. However, it is difficult to obtain these states due to complex adhesion mechanism and changeable operation environment. According to the influence of wheel/rail adhesion utilization on locomotive control action, the wheel/rail adhesion states are divided into four types, namely normal adhesion, fault indication, minor fault, and serious fault in this work. A wheel/rail adhesion state identification method based on particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is proposed. To this end, a wheel/rail state identification model is constructed using KELM, and then the regularization coefficient and kernel parameter of KELM are optimized by using PSO to improve its accuracy. Finally, based on the actual data, the proposed method is compared with PSO support vector machines (PSO-SVM) and basic KELM, respectively, and the results are given to verify the effectiveness and feasibility of the proposed method.

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Fault diagnosis method based on wavelet packet-energy entropy and fuzzy kernel extreme learning machine

Advances in Mechanical Engineering ◽

10.1177/1687814017751446 ◽

2018 ◽

Vol 10 (1) ◽

pp. 168781401775144 ◽

Cited By ~ 6

Author(s):

Jun Ma ◽

Jiande Wu ◽

Xiaodong Wang

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Extreme Learning Machine ◽

Wavelet Packet ◽

Support Vector ◽

Machine Method ◽

Energy Entropy ◽

Diagnosis Method ◽

Kernel Extreme Learning Machine ◽

Learning Machine

Aiming at connatural limitations of extreme learning machine in practice, a new fault diagnosis method based on wavelet packet-energy entropy and fuzzy kernel extreme learning machine is proposed. On one hand, the presented method can extract the more efficient features using the wavelet packet-energy entropy method, and on the other hand, the sample fuzzy membership degree matrix U, weight matrix W which is used to describe the sample imbalance, and the kernel function are introduced to construct the fuzzy kernel extreme learning machine model with high accuracy and reliability. The experimental results of rolling bearing and check valve are obtained and analyzed in MATLAB 2010b. The results show that the proposed fuzzy kernel extreme learning machine method can obtain fairly or slightly better classification performance than the traditional extreme learning machine, kernel extreme learning machine, back propagation, support vector machine, and fuzzy support vector machine.

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A Prediction Method for Deck Motion of Aircraft Carrier Based on Particle Swarm Optimization and Kernel Extreme Learning Machine

Sensors and Materials ◽

10.18494/sam.2017.1588 ◽

2017 ◽

pp. 1291

Keyword(s):

Particle Swarm Optimization ◽

Extreme Learning Machine ◽

Particle Swarm ◽

Prediction Method ◽

Aircraft Carrier ◽

Swarm Optimization ◽

Kernel Extreme Learning Machine ◽

Learning Machine

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Logic Learning Machine and standard supervised methods for Hodgkin’s lymphoma prognosis using gene expression data and clinical variables

Health Informatics Journal ◽

10.1177/1460458216655188 ◽

2016 ◽

Vol 24 (1) ◽

pp. 54-65 ◽

Cited By ~ 3

Author(s):

Stefano Parodi ◽

Chiara Manneschi ◽

Damiano Verda ◽

Enrico Ferrari ◽

Marco Muselli

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Support Vector Machine ◽

Decision Tree ◽

Hodgkin’S Lymphoma ◽

Hodgkin's Lymphoma ◽

Support Vector ◽

Expression Data ◽

Clinical Variables ◽

Learning Machine

This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin’s lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin’s lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms ( k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene ( XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin’s lymphoma patients.

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