scholarly journals Coupled Least Squares Support Vector Ensemble Machines

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 195 ◽  
Author(s):  
Dickson Keddy Wornyo ◽  
Xiang-Jun Shen
Keyword(s):  
Least Squares ◽  
Classification Performance ◽  
Support Vector ◽  
Vector Method ◽  
Wide Range ◽  
Good Classification ◽  
Coupling Strategy ◽  
Good Classification Performance ◽  
Data Driven Modeling ◽  
Model Approach

The least squares support vector method is a popular data-driven modeling method which shows better performance and has been successfully applied in a wide range of applications. In this paper, we propose a novel coupled least squares support vector ensemble machine (C-LSSVEM). The proposed coupling ensemble helps improve robustness and produce good classification performance than the single model approach. The proposed C-LSSVEM can choose appropriate kernel types and their parameters in a good coupling strategy with a set of classifiers being trained simultaneously. The proposed method can further minimize the total loss of ensembles in kernel space. Thus, we form an ensemble regressor by co-optimizing and weighing base regressors. Experiments conducted on several datasets such as artificial datasets, UCI classification datasets, UCI regression datasets, handwritten digits datasets and NWPU-RESISC45 datasets, indicate that C-LSSVEM performs better in achieving the minimal regression loss and the best classification accuracy relative to selected state-of-the-art regression and classification techniques.

IMPROVING SVM PERFORMANCE IN MULTI-LABEL DOMAINS: THRESHOLD ADJUSTMENT

2013 ◽  
Vol 22 (01) ◽  
pp. 1250038 ◽  
Author(s):  
PEERAPON VATEEKUL ◽  
SAREEWAN DENDAMRONGVIT ◽  
MIROSLAV KUBAT
Keyword(s):  
Classification Performance ◽  
Support Vector ◽  
Binary Classifier ◽  
Research Groups ◽  
Computational Costs ◽  
Vector Machines ◽  
Threshold Adjustment ◽  
Good Classification ◽  
Good Classification Performance ◽  
Suboptimal Behavior

In “multi-label domains,” where the same example can simultaneously belong to two or more classes, it is customary to induce a separate binary classifier for each class, and then use them all in parallel. As a result, some of these classifiers are induced from imbalanced training sets where one class outnumbers the other – a circumstance known to hurt some machine learning paradigms. In the case of Support Vector Machines (SVM), this suboptimal behavior is explained by the fact that SVM seeks to minimize error rate, a criterion that is in domains of this type misleading. This is why several research groups have studied mechanisms to readjust the bias of SVM's hyperplane. The best of these achieves very good classification performance at the price of impractically high computational costs. We propose here an improvement where these cost are reduced to a small fraction without significantly impairing classification.

Application of WAVELET-SVM in Fault Diagnosis for the UV Control System

2011 ◽  
Vol 65 ◽  
pp. 199-203
Author(s):  
Sheng Wu Wang ◽  
Xiu Hua Shi ◽  
Hui Xu ◽  
Zhao Jing Tong
Keyword(s):  
Control System ◽  
Fault Diagnosis ◽  
Wavelet Transformation ◽  
Classification Performance ◽  
Support Vector ◽  
Fault Features ◽  
Good Classification ◽  
Good Classification Performance ◽  
Simulation Results ◽  
Fault Characteristic

Wavelet Analysis extracts the main feature from the fault signal through wavelet transformation, so it is advantageous to withdraw fault characteristic for fault diagnosis. Support Vector Machine (SVM) has shown its good classification performance in fault diagnosis. A new method of fault diagnosis for UV control system based on WAVELET-SVM is raised. The sensor output is sampled in frequency domain and it is preprocessed by wavelet to extract main vectors of the fault features. Fault patterns under various states are classified using multi-class SVM, and fault diagnosis is realized. The simulation results show that WAVELET-SVM is feasible to detect and locate faults quickly and exactly and has high robustness.

Defect Classification in Pipes by Neural Networks Using Multiple Guided Ultrasonic Wave Features Extracted After Wavelet Processing

2005 ◽  
Vol 127 (3) ◽  
pp. 294-303 ◽  
Author(s):  
Piervincenzo Rizzo ◽  
Ivan Bartoli ◽  
Alessandro Marzani ◽  
Francesco Lanza di Scalea
Keyword(s):  
Guided Waves ◽  
Fourier Transforms ◽  
Damage Index ◽  
Classification Performance ◽  
Discrete Wavelet ◽  
Cross Sectional ◽  
Classification Framework ◽  
Discrete Wavelet Decomposition ◽  
Good Classification ◽  
Good Classification Performance

This paper casts pipe inspection by ultrasonic guided waves in a feature extraction and automatic classification framework. The specific defect under investigation is a small notch cut in an ASTM-A53-F steel pipe at depths ranging from 1% to 17% of the pipe cross-sectional area. A semi-analytical finite element method is first used to model wave propagation in the pipe. In the experiment, reflection measurements are taken and six features are extracted from the discrete wavelet decomposition of the raw signals and from the Hilbert and Fourier transforms of the reconstructed signals. A six-dimensional damage index is then constructed, and it is fed to an artificial neural network that classifies the size and the location of the notch. Overall, the wavelet-based multifeature analysis demonstrates good classification performance and robustness against noise and changes in some of the operating parameters.

scholarly journals Nondestructive Discrimination of Lead (Pb) in Preserved Eggs (Pidan) by Near-Infrared Spectroscopy and Chemometrics

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Lu Xu ◽  
Si-Min Yan ◽  
Chen-Bo Cai ◽  
Xiao-Ping Yu
Keyword(s):  
Least Squares ◽  
Inductively Coupled Plasma ◽  
Near Infrared ◽  
Safety Concern ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Nonlinear Least Squares ◽  
Classification Performance ◽  
Nondestructive Method ◽  
Support Vector

A major safety concern with pidan (preserved eggs) has been the usage of lead (II) oxide (PbO) during its processing. This paper develops a rapid and nondestructive method for discrimination of lead (Pb) in preserved eggs with different processing methods by near-infrared (NIR) spectroscopy and chemometrics. Ten batches of 331 unleaded eggs and six batches of 147 eggs processed with usage of PbO were collected and analyzed by NIR spectroscopy. Inductively coupled plasma mass spectrometry (ICP-MS) analysis was used as a reference method for Pb identification. The Pb contents of leaded eggs ranged from 1.2 to 12.8 ppm. Linear partial least squares discriminant analysis (PLSDA) and nonlinear least squares support vector machine (LS-SVM) were used to classify samples based on NIR spectra. Different preprocessing methods were studied to improve the classification performance. With second-order derivative spectra, PLSDA and LS-SVM obtained accurate and reliable classification of leaded and unleaded preserved eggs. The sensitivity and specificity of PLSDA were 0.975 and 1.000, respectively. Because the strictest safety standard of Pb content in traditional pidan is 2 ppm, the proposed method shows the feasibility for rapid and nondestructive discrimination of Pb in Chinese preserved eggs.

Convolutional Neural Networks for Water Body Extraction from Landsat Imagery

2017 ◽  
Vol 16 (01) ◽  
pp. 1750001 ◽  
Author(s):  
Long Yu ◽  
Zhiyin Wang ◽  
Shengwei Tian ◽  
Feiyue Ye ◽  
Jianli Ding ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Water Body ◽  
Spatial Information ◽  
Landsat Imagery ◽  
Classification Performance ◽  
Support Vector ◽  
Learning Methods ◽  
Wide Range

Traditional machine learning methods for water body extraction need complex spectral analysis and feature selection which rely on wealth of prior knowledge. They are time-consuming and hard to satisfy our request for accuracy, automation level and a wide range of application. We present a novel deep learning framework for water body extraction from Landsat imagery considering both its spectral and spatial information. The framework is a hybrid of convolutional neural networks (CNN) and logistic regression (LR) classifier. CNN, one of the deep learning methods, has acquired great achievements on various visual-related tasks. CNN can hierarchically extract deep features from raw images directly, and distill the spectral–spatial regularities of input data, thus improving the classification performance. Experimental results based on three Landsat imagery datasets show that our proposed model achieves better performance than support vector machine (SVM) and artificial neural network (ANN).

Experimental Study on Classification of High Carbon Fly Ash with a Hydrocyclone

2014 ◽  
Vol 955-959 ◽  
pp. 2779-2784
Author(s):  
Sheng Bing Zhang ◽  
Yi Liu ◽  
Yan Hong Zhang ◽  
Hua Lin Wang
Keyword(s):  
Fly Ash ◽  
Classification Performance ◽  
Raw Material ◽  
Unburned Carbon ◽  
Flotation Column ◽  
Wide Range ◽  
Large Particles ◽  
Good Classification Performance

The fly ash has a wide range of particles size and has a large amount of unburned carbon. As a raw material, the presence of the large particles and the unburned carbon will reduce the quality of the product. How to improve the utilization of fly ash has become one of focus points. In this work, hydrocyclone was adopted for classification after decarburization in a flotation column. Hydrocyclone showed a very good classification performance. In overflow, the particles were all smaller than 85μm. About 97.34% of the particles were smaller than 25 μm. Different fly ash content was selected to investigate its influence on the decarburization. The results showed that it made no obvious differences.

scholarly journals COMPARISON OF ALGORITHMS FOR CONTROLLED PIXEL-BY-PIXEL CLASSIFICATION OF NOISY MULTICHANNEL IMAGES

2019 ◽  
pp. 39-46
Author(s):  
Vladimir Lukin ◽  
Galina Proskura ◽  
Irina Vasilieva
Keyword(s):  
Logistic Regression ◽  
Multilayer Perceptron ◽  
Additive White Gaussian Noise ◽  
Classification Performance ◽  
Support Vector ◽  
Noisy Images ◽  
Heterogeneous Objects ◽  
Wide Range ◽  
Isolated Points

The subject of this study is the pixel-by-pixel controlled classification of multichannel satellite images distorted by additive white Gaussian noise. The paper aim is to study the effectiveness of various methods of image classification in a wide range of signal-to-noise ratios; an F-measure is used as a criterion for recognition efficiency. It is a harmonic mean of accuracy and completeness: accuracy shows how much of the objects identified by the classifier as positive are positive; completeness shows how much of the positive objects were allocated by the classifier. Tasks: generate random valuesof the brightness of the noise components, ensuring their compliance with the accepted probabilistic model; implement the procedures of element-wise controlled classification according to the methods of support vectors, logistic regression, neural network based on a multilayer perceptron for images distorted by noise; evaluate and analyze the results of objects bezel-wise classification of noisy images; investigate the effect of noise variance on classification performance. The following results are obtained. Algorithms of pixel-by-pixel controlled classification are implemented. A comparative analysis of classification efficiency in noisy images is performed. Conclusions are drawn. It is shown that all classifiers provide the best results for classes that mainly correspond to areal objects (Water, Grass) while heterogeneous objects (Urban and, especially, Bushes) are recognized in the worst way; classifiers based on the support vector machine and logistic regression show low recognition accuracy of extended objects, such as a narrow river (that belongs to the wide class of "water"). The presence of noise in the image leads to a significant increase in the number of recognition errors, which mainly appear as isolated points on the selected segments, that is, incorrectly classified pixels. In this case, the best value of the classification quality indicator is achieved using neural networks based on a multilayer perceptron.

scholarly journals Training Classifiers under Covariate Shift by Constructing the Maximum Consistent Distribution Subset

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Xu Yu ◽  
Miao Yu ◽  
Li-xun Xu ◽  
Jing Yang ◽  
Zhi-qiang Xie
Keyword(s):  
Subset Selection ◽  
Feature Space ◽  
Classification Performance ◽  
Selection Method ◽  
Experimental Results ◽  
Training Set ◽  
Traditional Methods ◽  
Generalization Ability ◽  
Good Classification ◽  
Good Classification Performance

The assumption that the training and testing samples are drawn from the same distribution is violated under covariate shift setting, and most algorithms for the covariate shift setting try to first estimate distributions and then reweight samples based on the distributions estimated. Due to the difficulty of estimating a correct distribution, previous methods can not get good classification performance. In this paper, we firstly present two types of covariate shift problems. Rather than estimating the distributions, we then desire an effective method to select a maximum subset following the target testing distribution based on feature space split from the auxiliary set or the target training set. Finally, we prove that our subset selection method can consistently deal with both scenarios of covariate shift. Experimental results demonstrate that training a classifier with the selected maximum subset exhibits good generalization ability and running efficiency over those of traditional methods under covariate shift setting.

The Transformer Fault Diagnosis Method Based on Improved Support Vector Machine

2013 ◽  
Vol 422 ◽  
pp. 83-88
Author(s):  
Chao Lin Huang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
High Reliability ◽  
Particle Swarm ◽  
Classification Performance ◽  
Support Vector ◽  
Good Classification Performance ◽  
Diagnosis Method ◽  
Transformer Fault ◽  
Improved Support Vector Machine

Aiming at the fault diagnosis problem, the transformers fault diagnosis method is proposed based on improved support vector machine. The optimum parameters setting are got by the particle swarm optimization. The experimental results demonstrate that the proposed method of this paper has the good classification performance, the high reliability, effective and feasible. Keywords: support vector machine, fault diagnosis, particle swarm, classification

scholarly journals Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1094 ◽  
Author(s):  
Moting Su ◽  
Zongyi Zhang ◽  
Ye Zhu ◽  
Donglan Zha
Keyword(s):  
Natural Gas ◽  
Least Squares ◽  
Mean Square Error ◽  
Environmental Conservation ◽  
Spot Price ◽  
Support Vector ◽  
Mean Square ◽  
Least Squares Regression ◽  
Spot Prices ◽  
Wide Range

Natural gas is often described as the cleanest fossil fuel. The consumption of natural gas is increasing rapidly. Accurate prediction of natural gas spot prices would significantly benefit energy management, economic development, and environmental conservation. In this study, the least squares regression boosting (LSBoost) algorithm was used for forecasting natural gas spot prices. LSBoost can fit regression ensembles well by minimizing the mean squared error. Henry Hub natural gas spot prices were investigated, and a wide range of time series from January 2001 to December 2017 was selected. The LSBoost method is adopted to analyze data series at daily, weekly and monthly. An empirical study verified that the proposed prediction model has a high degree of fitting. Compared with some existing approaches such as linear regression, linear support vector machine (SVM), quadratic SVM, and cubic SVM, the proposed LSBoost-based model showed better performance such as a higher R-square and lower mean absolute error, mean square error, and root-mean-square error.

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